Such underwater human-robot teaming is the focus of an MIT Lincoln Laboratory project funded through an internally administered R&D portfolio on autonomous systems and carried out by the Advanced Undersea Systems and Technology Group. The project seeks to leverage the respective strengths of humans and robots to optimize maritime missions for the U.S. military, including critical infrastructure inspection and repair, search and rescue, harbor entry, and countermine operations.

"Divers and AUVs generally don't team at all underwater," says principal investigator Madeline Miller. "Underwater missions requiring humans typically do so because they involve some sort of manipulation a robot can't do, like repairing infrastructure or deactivating a mine. Even ROVs are challenging to work with underwater in very skilled manipulation tasks because the manipulators themselves aren't agile enough."

Beyond their superior dexterity, humans excel at recognizing objects underwater. But humans working underwater can't perform complex computations or move very quickly, especially if they are carrying heavy equipment; robots have an edge over humans in processing power, high-speed mobility, and endurance. To combine these strengths, Miller and her team are developing hardware and algorithms for underwater navigation and perception — two key capabilities for effective human-robot teaming.

As Miller explains, divers may only have a compass and fin-kick counts to guide them. With few landmarks and potentially murky conditions caused by a lack of light at depth or the presence of biological matter in the water column, they can easily become disoriented and lost. For robots to help divers navigate, they need to perceive their environment. However, in the presence of darkness and turbidity, optical sensors (cameras) cannot generate images, while acoustic sensors (sonar) generate images that lack color and only show the shapes and shadows of objects in the scene. The historical lack of large, labeled sonar image datasets has hindered training of underwater perception algorithms. Even if data were available, the dynamic ocean can obscure the true nature of objects, confusing artificial intelligence. For instance, a downed aircraft broken into multiple pieces, or a tire covered in an overgrowth of mussels, may no longer resemble an aircraft or tire, respectively.

"Ultimately, we want to devise solutions for navigation and perception in expeditionary environments," Miller says. "For the missions we're thinking about, there is limited or no opportunity to map out the area in advance. For the harbor entry mission, maybe you have a satellite map but no underwater map, for example."

On the navigation side, Miller's team picked up on work started by the MIT Marine Robotics Group, led by John Leonard, to develop diver-AUV teaming algorithms. With their navigation algorithms, Leonard's group ran simulations under optimal conditions and performed field testing in calm waters using human-paddled kayaks as proxies for both divers and AUVs. Miller's team then integrated these algorithms into a mission-relevant AUV and began testing them under more realistic ocean conditions, initially with a support boat acting as a diver surrogate, and then with actual divers.

"We quickly learned that you need more sensing capabilities on the diver when you factor in ocean currents," Miller explains. "With the algorithms demonstrated by MIT, the vehicle only needed to calculate the distance, or range, to the diver at regular intervals to solve the optimization problem of estimating the positions of both the vehicle and diver over time. But with the real ocean forces pushing everything around, this optimization problem blows up quickly."

On the perception side, Miller's team has been developing an AI classifier that can process both optical and sonar data mid-mission and solicit human input for any objects classified with uncertainty.

"The idea is for the classifier to pass along some information — say, a bounding box around an image — to the diver and indicate, "I think this is a tire, but I'm not sure. What do you think?" Then, the diver can respond, "Yes, you've got it right, or no, look over here in the image to improve your classification," Miller says.

This feedback loop requires an underwater acoustic modem to support diver-AUV communication. State-of-the-art data rates in underwater acoustic communications would require tens of minutes to send an uncompressed image from the AUV to the diver. So, one aspect the team is investigating is how to compress information into a minimum amount to be useful, working within the constraints of the low bandwidth and high latency of underwater communications and the low size, weight, and power of the commercial off-the-shelf (COTS) hardware they're using. For their prototype system, the team procured mostly COTS sensors and built a sensor payload that would easily integrate into an AUV routinely employed by the U.S. Navy, with the goal of facilitating technology transition. Beyond sonar and optical sensors, the payload features an acoustic modem for ranging to the diver and several data processing and compute boards.

Miller's team has tested the sensor-equipped AUV and algorithms around coastal New England — including in the open ocean near Portsmouth, New Hampshire, with the University of New Hampshire's (UNH) Gulf Surveyor and Gulf Challenger coastal research vessels as diver surrogates, and on the Boston-area Charles River, with an MIT Sailing Pavilion skiff as the surrogate.

"The UNH boats are well-equipped and can access realistic ocean conditions. But pretending to be a diver with a large boat is hard. With the skiff, we can move more slowly and get the relative motion in tune with how a diver and AUV would navigate together."

Last summer, the team started testing equipment with human divers at Michigan Technological University's Great Lakes Research Center. Although the divers lacked an interface to feed back information to the AUV, each swam holding the team's tube-shaped prototype tablet, dubbed a "tube-let." The tube-let was equipped with a pressure and depth sensor, inertial measurement unit (to track relative motion), and ranging modem — all necessary components for the navigation algorithms to solve the optimization problem.

"A challenge during testing was coordinating the motion of the diver and vehicle, because they don't yet collaborate," Miller says. "Once the divers go underwater, there is no communication with the team on the surface. So, you have to plan where to put the diver and vehicle so they don't collide."

The team also worked on the perception problem. The water clarity of the Great Lakes at that time of year allowed for underwater imaging with an optical sensor. Caroline Keenan, a Lincoln Scholars Program PhD student jointly working in the laboratory's Advanced Undersea Systems and Technology Group and Leonard's research group at MIT, took the opportunity to advance her work on knowledge transfer from optical sensors to sonar sensors. She is exploring whether optical classifiers can train sonar classifiers to recognize objects for which sonar data doesn't exist. The motivation is to reduce the human operator load associated with labeling sonar data and training sonar classifiers.

With the internally funded research program coming to an end, Miller's team is now seeking external sponsorship to refine and transition the technology to military or commercial partners.

"The modern world runs on undersea telecommunication and power cables, which are vulnerable to attack by disruptive actors. The undersea domain is becoming increasingly contested as more nations develop and advance the capabilities of autonomous maritime systems. Maintaining global economic security and U.S. strategic advantage in the undersea domain will require leveraging and combining the best of AI and human capabilities," Miller says.

Like the fibers that bundle together to form biological muscles, these fibers can be arranged in different configurations to meet the demands of a given task. Unlike conventional robotic actuation systems, they are compliant enough to interface comfortably with the human body and operate silently without motors, external pumps, or other bulky supporting hardware.

The new electrofluidic fiber muscles — electrically driven actuators built in fiber format — are described in a recent paper published in Science Robotics. The work is led by Media Lab PhD candidate Ozgun Kilic Afsar; Vito Cacucciolo, a professor at the Politecnico di Bari; and four co-authors.

The new system brings together two technologies, Afsar explains. One is a fluidically driven artificial muscle known as a thin McKibben actuator, and the other is a miniaturized solid-state pump based on electrohydrodynamics (EHD), which can generate pressure inside a sealed fluid compartment without moving parts or an external fluid supply.

Until now, most fluid-driven soft actuators have relied on external “heavy, bulky, oftentimes noisy hydraulic infrastructure,” Afsar says, “which makes them difficult to integrate into systems where mobility or compact, lightweight design is important.” This has created a fundamental bottleneck in the practical use of fluidic actuators in real-world applications.

The key to breaking through that bottleneck was the use of integrated pumps based on electrohydrodynamic principles. These millimeter-scale, electrically driven pumps generate pressure and flow by injecting charge into a dielectric fluid, creating ions that drag the fluid along with them. Weighing just a few grams each and not much thicker than a toothpick, they can be fabricated continuously and scaled easily. “We integrated these fiber pumps into a closed fluidic circuit with the thin McKibben actuators,” Afsar says, noting that this was not a simple task given the different dynamics of the two components.

A key design strategy was to pair these fibers in what are known as antagonistic configurations. Cacucciolo explains that this is where “one muscle contracts while another elongates,” as when you bend your arm and your biceps contract while your triceps stretch. In their system, a millimeter-scale fiber pump sits between two similarly scaled McKibben actuators, driving fluid into one actuator to contract it while simultaneously relaxing the other.

“This is very much reminiscent of how biological muscles are configured and organized,” Afsar says. “We didn’t choose this configuration simply for the sake of biomimicry, but because we needed a way to store the fluid within the muscle design.” The need for an external reservoir open to the atmosphere has been one of the main factors limiting the practical use of EHD pumps in robotic systems outside the lab. By pairing two McKibben fibers in line, with a fiber pump between them to form a closed circuit, the team eliminated that need entirely.

Another key finding was that the muscle fibers needed to be pre-pressurized, rather than simply filled. “There is a minimum internal system pressure that the system can tolerate,” Afsar says, “below which the pump can degrade or temporarily stop working.” This happens because of cavitation, in which vapor bubbles form when the pressure at the pump inlet drops below the vapor pressure of the liquid, eventually leading to dielectric breakdown.

To prevent cavitation, they applied a “bias” pressure from the outset so that the pressure at the fiber pump inlet never falls below the liquid’s vapor pressure. The magnitude of this bias pressure can be adjusted depending on the application. “To achieve the maximum contraction the muscle can generate, we found there is a specific bias pressure range that is optimal,” she says. “If you want to configure the system for faster response, you might increase that bias pressure, though with some reduction in maximum contraction.”

Cacucciolo adds that most of today’s robotic limbs and hands are built around electric servo motors, whose configuration differs fundamentally from that of natural muscles. Servo motors generate rotational motion on a shaft that must be converted into linear movement, whereas muscle fibers naturally contract and extend linearly, as do these electrofluidic fibers. 

“Most robotic arms and humanoid robots are designed around the servo motors that drive them,” he says. “That creates integration constraints, because servo motors are hard to package densely and tend to concentrate mass near the joints they drive. By contrast, artificial muscles in fiber form can be packed tightly inside a robot or exoskeleton and distributed throughout the structure, rather than concentrated near a joint.”

These electrofluidic muscles may be especially useful for wearable applications, such as exoskeletons that help a person lift heavier loads or assistive devices that restore or augment dexterity. But the underlying principles could also apply more broadly. “Our findings extend to fluid-driven robotic systems in general,” Cacucciolo says. “Wherever fluidic actuators are used, or where engineers want to replace external pumps with internal ones, these design principles could apply across a wide range of fluid-driven robotic systems.”

This work “presents a major advancement in fiber-format soft actuation,” which “addresses several long-standing hurdles in the field, particularly regarding portability and power density,” says Herbert Shea, a professor in the Soft Transducers Laboratory at Ecole Polytechnique Federale de Lausanne in Switzerland, who was not associated with this research. “The lack of moving parts in the pump makes these muscles silent, a major advantage for prosthetic devices and assistive clothing,” he says.

Shea adds that “this high-quality and rigorous work bridges the gap between fundamental fluid dynamics and practical robotic applications. The authors provide a complete system-level solution — characterizing the individual components, developing a predictive physical model, and validating it through a range of demonstrators.”

In addition to Afsar and Cacucciolo, the team also included Gabriele Pupillo and Gennaro Vitucci at Politecnico di Bari and Wedyan Babatain and Professor Hiroshi Ishii at the MIT Media Lab. The work was supported by the European Research Council and the Media Lab’s multi-sponsored consortium.

Now, MIT engineers have designed an ultrasound wristband that precisely tracks a wearer’s hand movements in real-time. The wristband produces ultrasound images of the wrist’s muscles, tendons, and ligaments as the hand moves, and is paired with an artificial intelligence algorithm that continuously translates the images into the corresponding positions of the five fingers and palm.

The researchers can train the wristband to learn a wearer’s hand motions, which the device can communicate in real-time to a robot or a virtual environment.

In demonstrations, the team has shown that a person wearing the wristband can wirelessly control a robotic hand. As the person gestures or points, the robot does the same. In a sort of wireless marionette interaction, the wearer can manipulate the robot to play a simple tune on the piano and shoot a small basketball into a desktop hoop. With the same wristband, a wearer can also manipulate objects on a computer screen, for instance pinching their fingers together to enlarge and minimize a virtual object.

The team is using the wristband to gather hand motion data from many more users with different hand sizes, finger shapes, and gestures. They envision building a large dataset of hand motions that can be plumbed, for instance, to train humanoid robots in dexterity tasks, such as performing certain surgical procedures. The ultrasound band could also be used to grasp, manipulate, and interact with objects in video games, design applications, or other virtual settings.

“We think this work has immediate impact in potentially replacing hand tracking techniques with wearable ultrasound bands in virtual and augmented reality,” says Xuanhe Zhao, the Uncas and Helen Whitaker Professor of Mechanical Engineering at MIT. “It could also provide huge amounts of training data for dexterous humanoid robots.”

Zhao, Gengxi Lu, and their colleagues present the wristband’s new design in a paper appearing today in Nature Electronics. Their MIT co-authors are former postdocs Xiaoyu Chen, Shucong Li, and Bolei Deng; graduate students SeongHyeon Kim and Dian Li; postdocs Shu Wang and Runze Li; and Anantha Chandrakasan, MIT provost and the Vannevar Bush Professor of Electrical Engineering and Computer Science. Other co-authors are graduate students Yushun Zheng and Junhang Zhang, Baoqiang Liu, Chen Gong, and Professor Qifa Zhou from the University of Southern California.

Seeing strings

There are currently a number of approaches to capturing and mimicking human hand dexterity in robots. Some approaches use cameras to record a person’s hand movements as they manipulate objects or perform tasks. Others involve having a person wear a glove with sensors, which records the person’s hand movements and transmits the data to a receiving robot. But erecting a complex camera system for different applications is impractical and prone to visual obstacles. And sensor-laden gloves could limit a person’s natural hand motions and sensations.

A third approach uses the electrical signals from muscles in the wrist or forearm that scientists then correlate with specific hand movements. Researchers have made significant advances in this approach, however these signals are easily affected by noise in the environment. They are also not sensitive enough to distinguish subtle changes in movements. For instance, they may discern whether a thumb and index finger are pinched together or pulled apart, but not much of the in-between path.

Zhao’s team wondered whether ultrasound imaging might capture more dexterous and continuous hand movements. His group has been developing various forms of ultrasound stickers — miniaturized versions of the transducers used in doctor’s offices that are paired with hydrogel material that can safely stick to skin.

In their new study, the team incorporated the ultrasound sticker design into a wearable wristband to continuously image the muscles and tendons in the wrist.

“The tendons and muscles in your wrist are like strings pulling on puppets, which are your fingers,” Lu says. “So the idea is: Each time you take a picture of the state of the strings, you’ll know the state of the hand.”

Mapping manipulation

The team designed a wristband with an ultrasound sticker that is the size of a smartwatch, and added onboard electronics that are about as small as a cellphone. They attached the wristband to a volunteer’s wrist and confirmed that the device produced clear and continuous images of the wrist as the volunteer moved their fingers in various gestures.

The challenge then was to relate the black and white ultrasound images of the wrist to specific positions of the hand. As it turns out, the fingers and thumb are capable of 22 degrees of freedom, or different ways of extending or angling. The researchers found that they could identify specific regions in their ultrasound images of the wrist that correlate to each of these 22 degrees of freedom. For instance, changes in one region relate to thumb extension, while changes in another region correlate with movements of the index finger.

To establish these connections, a volunteer wearing the wristband would move their hand in various positions while the researchers recorded the gestures with multiple cameras surrounding the volunteer. By matching changes in certain regions of the ultrasound images with hand positions recorded by the cameras, the team could label wrist image regions with the corresponding degree of freedom in the hand. But to do this translation continuously, and in real-time, would be an impossible task for humans.

So, the team turned to artificial intelligence. They used an AI algorithm that can be trained to recognize image patterns and correlate them with specific labels and, in this case, the hand’s various degrees of freedom. The researchers trained the algorithm with ultrasound images that they meticulously labeled, annotating the image regions associated with a specific degree of freedom. They tested the algorithm on a new set of ultrasound images and found it correctly predicted the corresponding hand gestures.

Once the researchers successfully paired the AI algorithm with the wristband, they tested the device on more volunteers. For the new study, eight volunteers with different hand and wrist sizes wore the wristband while they formed various hand gestures and grasps, including making the signs for all 26 letters in American Sign Language. They also held objects such as a tennis ball, a plastic bottle, a pair of scissors, and a pencil. In each case, the wristband precisely tracked and predicted the position of the hand.

To demonstrate potential applications, the team developed a simple computer program that they wirelessly paired with the wristband. As a wearer went through the motions of pinching and grasping, the gestures corresponded to zooming in and out on an object on the computer screen, and virtually moving and manipulating it in a smooth and continuous fashion.

The researchers also tested the wristband as a wireless controller of a simple commercial robotic hand. While wearing the wristband, a volunteer went through the motions of playing a keyboard. The robot in turn mimicked the motions in real-time to play a simple tune on a piano. The same robot was also able to mimic a person’s finger taps to play a desktop basketball game.

Zhao is planning to further miniaturize the wristband’s hardware, as well as train the AI software on many more gestures and movements from volunteers with wider ranging hand sizes and shapes. Ultimately, the team is building toward a wearable hand tracker that can be worn by anyone, to wirelessly manipulate humanoid robots or virtual objects with high dexterity.

“We believe this is the most advanced way to track dexterous hand motion, through wearable imaging of the wrist,” Zhao says. “We think these wearable ultrasound bands can provide intuitive and versatile controls for virtual reality and robotic hands.”

This research was supported, in part, by MIT, the U.S. National Institutes of Health, the U.S. National Science Foundation, the U.S. Department of Defense, and Singapore National Research Foundation through the Singapore-MIT Alliance for Research and Technology.

At MIT’s Center for Bits and Atoms (CBA) in late January, about a dozen students explored that possibility during an Independent Activities Period (IAP) workshop on Raman spectroscopy, a technique that uses laser light to “fingerprint” materials. The session even featured a robotic dog equipped with sensing equipment, demonstrating how chemical analysis can be done remotely.

The workshop, led by MIT postdoc Lamyaa Almehmadi in collaboration with the CBA, introduced participants to a powerful technique now used by law enforcement and first responders to identify narcotics and explosives, by gemologists to authenticate precious stones, and pharmaceutical companies to verify raw materials and ensure product quality. CBA graduate researcher Jiaming Liu co-hosted, delivering lectures, demonstrating Raman equipment, and contributing to the curriculum and hands-on demonstrations.

“It can open up new possibilities for innovation across many fields,” said Almehmadi, an analytical chemist in the Department of Materials Science and Engineering (DMSE). After attendees learned the fundamentals, she encouraged them to think creatively about new applications: “My hope is to inspire all of you to think about doing something with Raman spectroscopy that no one has done before.”

Fingerprinting materials

Participants brought items to class to analyze using handheld devices, which fire laser light and measure how it bounces back. The resulting pattern behaves like a molecular fingerprint, identifying the materials in the item — whether it’s a paper clip, a piece of tree bark, or a mixing bowl.

Workshop attendee Sarah Ciriello, an administrative assistant at DMSE who brought a stone she found at the beach, was taken aback by the results. The Raman device suggested a 39 percent probability that the sample contained concrete-like material, with the remaining readings matching synthetic compounds — blurring the line between natural and manufactured materials.

“It’s man-made — I was surprised,” Ciriello said.

Developed in 1928 by Indian scientist C.V. Raman, who later won the Nobel Prize in Physics, Raman spectroscopy was groundbreaking because it used visible light to probe materials without destroying them, a major advantage over other techniques at the time, such as chromatography or mass spectrometry. But for decades, the Raman signal — the light scattered back from a sample — was weak, and the instruments were big and bulky, limiting its practical use.

Advances in lasers, computing power, and miniaturized optics have transformed Raman spectroscopy into a portable tool. Today’s handheld devices can instantly compare a sample’s molecular fingerprint against vast digital libraries, allowing users to identify thousands of materials in seconds. Because it doesn’t destroy the sample, Raman is especially useful in fields that require preserving materials — such as law enforcement, where evidence must remain intact, and art restoration.

Almehmadi’s own research focuses on advancing Raman spectroscopy by developing highly sensitive, semiconductor-based sensors that make portable chemical analysis possible, with applications ranging from medical diagnostics to forensic and environmental monitoring.

“Raman can be used to analyze any material,” Almehmadi says. “That’s why I decided to introduce it to students from diverse backgrounds.”

IAP classes are open to students and staff across MIT, and the Raman workshop reflected that range — from administrative staff to graduate and undergraduate students and postdocs in departments and labs including DMSE, the Department of Mechanical Engineering, the Media Lab, and the Broad Institute.

Walking the robot dog

A crowd-pleasing element in the workshop was the integration of a robot dog that belongs to the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The demonstration highlighted how Raman technology can be used in dangerous environments, such as crime scenes or toxic industrial sites.

The handheld device was secured to the robot using tape, and Almehmadi showed how she could navigate the dog to a plastic bag filled with a white powder — baking soda.

But in a real-world scenario, “How can we know if it is baking soda or not?” she says. “So we just shined the light, and then the instrument told us what it was.”

Participants used a Wi-Fi app on their phones to view the results and a small remote controller to operate the robotic dog themselves.

“I loved the robot dog,” Ciriello says. “I was able to control it a bit, but it was challenging because the gauge was really sensitive.”

Michael Kitcher, a postdoc in DMSE, also praises the robot demonstration.

“Given that we just duct taped the device onto the dog — it was cool to see it actually worked,” he says.

Looking ahead

Kitcher, who researches magnetic materials for electronic applications, joined the workshop to learn more about Raman spectroscopy, which he had read about but never used. He was impressed by its versatility — in addition to the beach stone and baking soda, the device identified materials in a contact lens, cosmetics, and even a diamond.

Although it struggled to analyze a piece of chocolate he brought — other signals from the chocolate interfered — Kitcher sees strong potential for his own research. One area he’s interested in is unconventional magnetic materials, such as altermagnets, with unusual magnetic behavior that researchers hope to better understand and control for more energy-efficient electronics.

“Over the last couple of years, people have been trying to get a better sense of why these materials behave the way they do — how we can control this unconventional magnetic order,” he says. Raman spectroscopy can probe the vibrations of atoms in a material, helping researchers detect patterns in the crystal structure that underlie unusual magnetic behaviors. By understanding these vibrations, scientists could unlock material design rules that enable ultra-fast, low-energy computing.

Hands-on workshops like this — that inspire innovative future applications — Almehmadi says, are at the heart of an MIT education.

“I’ve always learned best by doing,” she says. “Lectures and reading are important, but real understanding comes from hands-on experience.”

Their method uses a specialized vision-language model to perceive the scenario in an image and simulate actions needed to reach a goal. Then a second model translates those simulations into a standard programming language for planning problems, and refines the solution.

In the end, the system automatically generates a set of files that can be fed into classical planning software, which computes a plan to achieve the goal. This two-step system generated plans with an average success rate of about 70 percent, outperforming the best baseline methods that could only reach about 30 percent.

Importantly, the system can solve new problems it hasn’t encountered before, making it well-suited for real environments where conditions can change at a moment’s notice.

“Our framework combines the advantages of vision-language models, like their ability to understand images, with the strong planning capabilities of a formal solver,” says Yilun Hao, an aeronautics and astronautics (AeroAstro) graduate student at MIT and lead author of an open-access paper on this technique. “It can take a single image and move it through simulation and then to a reliable, long-horizon plan that could be useful in many real-life applications.”

She is joined on the paper by Yongchao Chen, a graduate student in the MIT Laboratory for Information and Decision Systems (LIDS); Chuchu Fan, an associate professor in AeroAstro and a principal investigator in LIDS; and Yang Zhang, a research scientist at the MIT-IBM Watson AI Lab. The paper will be presented at the International Conference on Learning Representations.

Tackling visual tasks

For the past few years, Fan and her colleagues have studied the use of generative AI models to perform complex reasoning and planning, often employing large language models (LLMs) to process text inputs.

Many real-world planning problems, like robotic assembly and autonomous driving, have visual inputs that an LLM can’t handle well on its own. The researchers sought to expand into the visual domain by utilizing vision-language models (VLMs), powerful AI systems that can process images and text.

But VLMs struggle to understand spatial relationships between objects in a scene and often fail to reason correctly over many steps. This makes it difficult to use VLMs for long-range planning.

On the other hand, scientists have developed robust, formal planners that can generate effective long-horizon plans for complex situations. However, these software systems can’t process visual inputs and require expert knowledge to encode a problem into language the solver can understand.

Fan and her team built an automatic planning system that takes the best of both methods. The system, called VLM-guided formal planning (VLMFP), utilizes two specialized VLMs that work together to turn visual planning problems into ready-to-use files for formal planning software.

The researchers first carefully trained a small model they call SimVLM to specialize in describing the scenario in an image using natural language and simulating a sequence of actions in that scenario. Then a much larger model, which they call GenVLM, uses the description from SimVLM to generate a set of initial files in a formal planning language known as the Planning Domain Definition Language (PDDL).

The files are ready to be fed into a classical PDDL solver, which computes a step-by-step plan to solve the task. GenVLM compares the results of the solver with those of the simulator and iteratively refines the PDDL files.

“The generator and simulator work together to be able to reach the exact same result, which is an action simulation that achieves the goal,” Hao says.

Because GenVLM is a large generative AI model, it has seen many examples of PDDL during training and learned how this formal language can solve a wide range of problems. This existing knowledge enables the model to generate accurate PDDL files.

A flexible approach

VLMFP generates two separate PDDL files. The first is a domain file that defines the environment, valid actions, and domain rules. It also produces a problem file that defines the initial states and the goal of a particular problem at hand.

“One advantage of PDDL is the domain file is the same for all instances in that environment. This makes our framework good at generalizing to unseen instances under the same domain,” Hao explains.

To enable the system to generalize effectively, the researchers needed to carefully design just enough training data for SimVLM so the model learned to understand the problem and goal without memorizing patterns in the scenario. When tested, SimVLM successfully described the scenario, simulated actions, and detected if the goal was reached in about 85 percent of experiments.

Overall, the VLMFP framework achieved a success rate of about 60 percent on six 2D planning tasks and greater than 80 percent on two 3D tasks, including multirobot collaboration and robotic assembly. It also generated valid plans for more than 50 percent of scenarios it hadn’t seen before, far outpacing the baseline methods.

“Our framework can generalize when the rules change in different situations. This gives our system the flexibility to solve many types of visual-based planning problems,” Fan adds.

In the future, the researchers want to enable VLMFP to handle more complex scenarios and explore methods to identify and mitigate hallucinations by the VLMs.

“In the long term, generative AI models could act as agents and make use of the right tools to solve much more complicated problems. But what does it mean to have the right tools, and how do we incorporate those tools? There is still a long way to go, but by bringing visual-based planning into the picture, this work is an important piece of the puzzle,” Fan says.

This work was funded, in part, by the MIT-IBM Watson AI Lab.

“This problem is in no way solved, in industry or even in research settings,” says Nicholas Roy, the Jerome C. Hunsaker Professor in the MIT Department of Aeronautics and Astronautics (AeroAstro). “You’ve got to bring together a lot of pieces of code, software, and integrate multiple pieces of hardware. Putting those together is not trivial.”

Not trivial, but for students nearing the culmination of their Course 16 undergraduate careers, far from impossible. In class 16.85 Autonomy Capstone (Design and Testing of Autonomous Vehicles), students design, implement, deploy, and test a full software architecture for flying autonomous systems. These systems have wide-ranging applications, from urban air-mobility and reusable launch vehicles to extraterrestrial exploration. With robust autonomous technology, vehicles can operate far from home while engineers watch from mission control centers not too different from the high bay in AeroAstro’s Kresa Center for Autonomous Systems.

Roy and Jonathan How, Ford Professor of Engineering, developed the new course to build on the foundations of class 16.405 (Robotics: Science and Systems), which introduces students to working with complex robotic platforms and autonomous navigation through ground vehicles with pre-built software. 16.85 applies those same principles to flight, with a basic quadrotor drone and an entirely blank slate to build their own navigation systems. The vehicles are then tested on an obstacle course featuring dubious landing pads and uncertain terrain. Students work in large teams (for this first run, two teams of seven — the SLAMdunkers and the Spelunkers) designed to mirror real-world missions where coordination across roles is essential. 

“The vehicles need to be able to differentiate between all these hidden risks that are in the mission and the environment that they’re in and still survive,” says How. “We really want the students to learn how to make a system that they have confidence in.”

Mission: Figure it out, together

“The specific mission we gave them this semester is to imagine that you are an aircraft of some kind, and you’ve got to go and explore the surface of an extraterrestrial body like Mars or the moon,” Roy explains. “You need to use onboard sensors to fly around and explore, build a map, identify interesting objects, and then land safely on what is probably not a flat surface, or not a perfectly horizontal surface.”

A mission of this magnitude is far too complex for any one engineer to tackle alone, but that too poses a challenge for a large team. “The hardest problems these days are coordination problems,” says Andrew Fishberg, a graduate student in the Aerospace Controls Laboratory and one of three teaching assistants (TAs) for the course. “To use the robotics term, a team of this size is something of a heterogeneous swarm. Not everyone has the same skill set, but everyone shows up with something to contribute, and managing that together is a challenge.”

The challenge asks students to apply multiple types of “systems thinking” to the task. Relationships, interdependencies, and feedback loops are critical to their software architecture, and equally important in how students communicate and coordinate with their teammates. “Writing the reports and communicating with a team feels like overhead sometimes, but if you don’t communicate, you have a team of one,” says Fishberg. “We don’t have these ‘solo inventor’ situations where one person figures everything out anymore — it’s hundreds of people building this huge thing.”

The new faces of flight

Students in the class say they are eager to enter the rapidly evolving field, working with unconventional tools and vehicles that go beyond traditional applications.

“We continue to send rovers to extraterrestrial bodies. But there is an increasing interest in deploying unmanned systems to explore Earth,” says Roy. “There’s lots of places on Earth where we want to send robots to go and explore, places where it’s hazardous for humans to go.” That expanding set of applications is exactly what draws students to the field.

“I was really excited for the idea of a new class, especially one that was focused on autonomy, because that’s where I see my career going,” says senior Norah Miller. “This class has given me a really great experience in what it feels like to develop software from zero to a full flying mission.”

The Design and Testing of Autonomous Vehicles course offers a unique perspective for instructors and TAs who have known many of the students throughout their undergraduate careers. As a capstone, it provides an opportunity to see that growth come full circle. “A couple years ago we’re solving differential equations, and now they’re implementing software they wrote on a quadrotor in the high bay,” says How.

After weeks of learning, building, testing, refinement, and finally, flight, the results reflected the goals of the course. “It was exactly what we wanted to see happen,” says Roy. “We gave them a pretty challenging mission. We gave them hardware that should be capable of completing the mission, but not guaranteed. And the students have put in a tremendous amount of effort and have really risen to the challenge.”

This breakthrough brings soft robotics closer to human-like adaptability for real-world applications, such as in assistive robotics, rehabilitation robots, and wearable or medical soft robots, by making them more intelligent, versatile, and safe.

The work was led by the Mens, Manus and Machina (M3S) interdisciplinary research group — a play on the Latin MIT motto “mens et manus,” or “mind and hand,” with the addition of “machina” for “machine” — within the Singapore-MIT Alliance for Research and Technology. Co-leading the project are researchers from the National University of Singapore (NUS), alongside collaborators from MIT and Nanyang Technological University in Singapore (NTU Singapore).

Unlike regular robots that move using rigid motors and joints, soft robots are made from flexible materials such as soft rubber and move using special actuators — components that act like artificial muscles to produce physical motion. While their flexibility makes them ideal for delicate or adaptive tasks, controlling soft robots has always been a challenge because their shape changes in unpredictable ways. Real-world environments are often complicated and full of unexpected disturbances, and even small changes in conditions — like a shift in weight, a gust of wind, or a minor hardware fault — can throw off their movements. 

Despite substantial progress in soft robotics, existing approaches often can only achieve one or two of the three capabilities needed for soft robots to operate intelligently in real-world environments: using what they’ve learned from one task to perform a different task, adapting quickly when the situation changes, and guaranteeing that the robot will stay stable and safe while adapting its movements. This lack of adaptability and reliability has been a major barrier to deploying soft robots in real-world applications until now.

In an open-access study titled “A general soft robotic controller inspired by neuronal structural and plastic synapses that adapts to diverse arms, tasks, and perturbations,” published Jan. 6 in Science Advances, the researchers describe how they developed a new AI control system that allows soft robots to adapt across diverse tasks and disturbances. The study takes inspiration from the way the human brain learns and adapts, and was built on extensive research in learning-based robotic control, embodied intelligence, soft robotics, and meta-learning.

The system uses two complementary sets of “synapses” — connections that adjust how the robot moves — working in tandem. The first set, known as “structural synapses”, is trained offline on a variety of foundational movements, such as bending or extending a soft arm smoothly. These form the robot’s built‑in skills and provide a strong, stable foundation. The second set, called “plastic synapses,” continually updates online as the robot operates, fine-tuning the arm’s behavior to respond to what is happening in the moment. A built-in stability measure acts like a safeguard, so even as the robot adjusts during online adaptation, its behavior remains smooth and controlled.

“Soft robots hold immense potential to take on tasks that conventional machines simply cannot, but true adoption requires control systems that are both highly capable and reliably safe. By combining structural learning with real-time adaptiveness, we’ve created a system that can handle the complexity of soft materials in unpredictable environments,” says MIT Professor Daniela Rus, co-lead principal investigator at M3S, director of the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-corresponding author of the paper. “It’s a step closer to a future where versatile soft robots can operate safely and intelligently alongside people — in clinics, factories, or everyday lives.”

“This new AI control system is one of the first general soft-robot controllers that can achieve all three key aspects needed for soft robots to be used in society and various industries. It can apply what it learned offline across different tasks, adapt instantly to new conditions, and remain stable throughout — all within one control framework,” says Associate Professor Zhiqiang Tang, first author and co-corresponding author of the paper who was a postdoc at M3S and at NUS when he carried out the research and is now an associate professor at Southeast University in China (SEU China).

The system supports multiple task types, enabling soft robotic arms to execute trajectory tracking, object placement, and whole-body shape regulation within one unified approach. The method also generalizes across different soft-arm platforms, demonstrating cross-platform applicability. 

The system was tested and validated on two physical platforms — a cable-driven soft arm and a shape-memory-alloy–actuated soft arm — and delivered impressive results. It achieved a 44–55 percent reduction in tracking error under heavy disturbances; over 92 percent shape accuracy under payload changes, airflow disturbances, and actuator failures; and stable performance even when up to half of the actuators failed. 

“This work redefines what’s possible in soft robotics. We’ve shifted the paradigm from task-specific tuning and capabilities toward a truly generalizable framework with human-like intelligence. It is a breakthrough that opens the door to scalable, intelligent soft machines capable of operating in real-world environments,” says Professor Cecilia Laschi, co-corresponding author and principal investigator at M3S, Provost’s Chair Professor in the NUS Department of Mechanical Engineering at the College of Design and Engineering, and director of the NUS Advanced Robotics Centre.

This breakthrough opens doors for more robust soft robotic systems to develop manufacturing, logistics, inspection, and medical robotics without the need for constant reprogramming — reducing downtime and costs. In health care, assistive and rehabilitation devices can automatically tailor their movements to a patient’s changing strength or posture, while wearable or medical soft robots can respond more sensitively to individual needs, improving safety and patient outcomes.

The researchers plan to extend this technology to robotic systems or components that can operate at higher speeds and more complex environments, with potential applications in assistive robotics, medical devices, and industrial soft manipulators, as well as integration into real-world autonomous systems.

The research conducted at SMART was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise program.

“This cell settling, which becomes worse during the long print sessions required to print large tissues, leads to clogged nozzles, uneven cell distribution, and inconsistencies between printed tissues,” explains Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering and assistant professor of mechanical engineering at MIT. “Existing solutions, such as manually stirring bioinks before loading them into the printer, or using passive mixers, cannot maintain uniformity once printing begins.”

In a study published Feb. 2 in the journal Device, Raman’s team introduces a new approach that aims to solve this core limitation by actively preventing cell sedimentation within bioinks during printing, allowing for more reliable and biologically consistent 3D printed tissues.

“Precise control over the bioink’s physical and biological properties is essential for recreating the structure and function of native tissues,” says Ferdows Afghah, a postdoc in mechanical engineering at MIT and lead author of the study.

“If we can print tissues that more closely mimic those in our bodies, we can use them as models to understand more about human diseases, or to test the safety and efficacy of new therapeutic drugs,” adds Raman. Such models could help researchers move away from techniques like animal testing, which supports recent interest from the U.S. Food and Drug Administration in developing faster, less expensive, and more informative new approaches to establish the safety and efficacy of new treatment paths.

“Eventually, we are working towards regenerative medicine applications such as replacing diseased or injured tissues in our bodies with 3D printed tissues that can help restore healthy function,” says Raman.

MagMix, a magnetically actuated mixer, is composed of two parts: a small magnetic propeller that fits inside the syringes used by bioprinters to deposit bioinks, layer by layer, into 3D tissues, and a permanent magnet attached to a motor that moves up and down near the syringe, controlling the movement of the propeller inside. Together, this compact system can be mounted onto any standard 3D bioprinter, keeping bioinks uniformly mixed during printing without changing the bioink formulation or interfering with the printer’s normal operation. To test the approach, the team used computer simulations to design the optimal mixing propeller geometry and speed and then validated its performance experimentally.

“Across multiple bioink types, MagMix prevented cell settling for more than 45 minutes of continuous printing, reducing clogging and preserving high cell viability,” says Raman. “Importantly, we showed that mixing speeds could be adjusted to balance effective homogenization for different bioinks while inducing minimal stress on the cells. As a proof-of-concept, we demonstrated that MagMix could be used to 3D print cells that could mature into muscle tissues over the course of several days.”

By maintaining uniform cell distribution throughout long or complex print jobs, MagMix enables the fabrication of high-quality tissues with more consistent biological function. Because the device is compact, low-cost, customizable, and easily integrated into existing 3D printers, it offers a broadly accessible solution for laboratories and industries working toward reproducible engineered tissues for applications in human health including disease modeling, drug screening, and regenerative medicine.

This work was supported, in part, by the Safety, Health, and Environmental Discovery Lab (SHED) at MIT, which provides infrastructure and interdisciplinary expertise to help translate biofabrication innovations from lab-scale demonstrations to scalable, reproducible applications.

“At the SHED, we focus on accelerating the translation of innovative methods into practical tools that researchers can reliably adopt,” says Tolga Durak, the SHED’s founding director. “MagMix is a strong example of how the right combination of technical infrastructure and interdisciplinary support can move biofabrication technologies toward scalable, real-world impact.”

The SHED’s involvement reflects a broader vision of strengthening technology pathways that enhance reproducibility and accessibility across engineering and the life sciences by providing equitable access to advanced equipment and fostering cross-disciplinary collaboration.

“As the field advances toward larger-scale and more standardized systems, integrated labs like SHED are essential for building sustainable capacity,” Durak adds. “Our goal is not only to enable discovery, but to ensure that new technologies can be reliably adopted and sustained over time.”

The team is also interested in non-medical applications of engineered tissues, such as using printed muscles to power safer and more efficient “biohybrid” robots.

The researchers believe this work can improve the reliability and scalability of 3D bioprinting, making the potential impacts on the field of 3D bioprinting and on human health significant. Their paper, “Advancing Bioink Homogeneity in Extrusion 3D Bioprinting with Active In Situ Magnetic Mixing,” is available now from the journal Device. 

The brainchild of senior Julianna Schneider and other students in the MIT Schwarzman College of Computing Undergraduate Advisory Group (UAG), NerdXing lets students search for a class and see all the other classes students have gone on to take in the past, including options that are off the beaten track.

“I hope that NerdXing will democratize course knowledge for everyone,” Schneider says. “I hope that for anyone who's a freshman and maybe hasn't picked their major yet, that they can go to NerdXing and start with a class that they would maybe never consider — and then discover that, ‘Oh wait, this is perfect for this really particular thing I want to study.’”

As a student double-majoring in artificial intelligence and decision-making and in mathematics, and doing research in the Biomimetic Robotics Laboratory in the Department of Mechanical Engineering, Schneider knows the benefits of interdisciplinary studies. It’s a part of the reason why she joined the UAG, which advises the MIT Schwarzman College of Computing’s leadership as it advances education and research at the intersections between computing, engineering, the arts, and more.

Through all of her activities, Schneider seeks to make people’s lives better through technology.

“This process of finding a problem in my community and then finding the right technology to solve that — that sort of approach and that framework is what guides all the things I do,” Schneider says. “And even in robotics, the things that I care about are guided by the sort of skills that I think we need to develop to be able to have meaningful applications.”

From Albania to MIT

Before she ever touched a robot or wrote code, Schneider was an accomplished young classical pianist in Albania. When she discovered her passion for robotics at age 13, she applied some of the skills she had learned while playing piano.

“I think on some fundamental level, when I was a pianist, I thought constantly about my motor dynamics as a human being, and how I execute really complex skills but do it over and over again at the top of my ability,” Schneider says. “When it came to robotics, I was building these robotic arms that also had to operate at the top of their ability every time and do really complex tasks. It felt kind of similar to me, like a fun crossover.”

Schneider joined her high school’s robotics team as a middle schooler, and she was so immediately enamored that she ended up taking over most of the coding and building of the team’s robot. She went on to win 14 regional and national awards across the three teams she led throughout middle and high school. It was clear to her that she’d found her calling.

NerdXing wasn’t Schneider’s first experience building new technology. At just 16, she built an app meant to connect English-speaking volunteers from her international school in Tirana, Albania, to local charities that only posted jobs in Albanian. By last year, the platform, called VoluntYOU, had 18 ambassadors across four continents. It has enabled volunteers to give out more than 2,000 burritos in Reno, Nevada; register hundreds of signatures to support women’s rights legislation in Albania; and help with administering Covid-19 vaccines to more than 1,200 individuals a day in Italy.

Schneider says her experience at an international school encouraged her to recognize problems and solutions all around her.

“When I enter a new community and I can immediately be like, ‘Oh wait, if we had this tool, that would be so cool and that would help all these people,’ I think that’s just a derivative of having grown up in a place where you hear about everyone’s super different life experiences,” she says.

Schneider describes NerdXing as a continuation of many of the skills she picked up while building VoluntYOU.

“They were both motivated by seeing a challenge where I thought, ‘Wait, we have the tech skills to build that. This is something that I can envision the solution to.’ And then I wanted to actually go and make that a reality,” Schneider says.

Robotics with a positive impact

At MIT, Schneider started working in the Biomimetic Robotics Laboratory of Professor Sangbae Kim, where she has now participated in three research projects, one of which she’s co-authoring a paper on. She’s part of a team that tests how robots, including the famous back-flipping mini cheetah, move, in order to see how they could complement humans in high-stakes scenarios.

Most of her work has revolved around crafting controllers, including one hybrid-learning and model-based controller that is well-suited to robots with limited onboard computing capacity. It would allow the robot to be used in regions with less access to technology.

“It’s not just doing technology for technology's sake, but because it will bridge out into the world and make a positive difference. I think legged robotics have some of the best potential to actually be a robotic partner to human beings in the scenarios that are most high-stakes,” Schneider says.

Schneider hopes to further robotic capabilities so she can find applications that will service communities around the world. One of her goals is to help create tools that allow a surgeon to operate on a patient a long distance away. 

To take a break from academics, Schneider has channeled her love of the arts into MIT’s vibrant social dancing scene. This year, she’s especially excited about country line dancing events where the music comes on and students have to guess the choreography.

“I think it's a really fun way to make friends and to connect with the community,” she says.

Inspired by vines’ twisty tenacity, engineers at MIT and Stanford University have developed a robotic gripper that can snake around and lift a variety of objects, including a glass vase and a watermelon, offering a gentler approach compared to conventional gripper designs. A larger version of the robo-tendrils can also safely lift a human out of bed.

The new bot consists of a pressurized box, positioned near the target object, from which long, vine-like tubes inflate and grow, like socks being turned inside out. As they extend, the vines twist and coil around the object before continuing back toward the box, where they are automatically clamped in place and mechanically wound back up to gently lift the object in a soft, sling-like grasp.

The researchers demonstrated that the vine robot can safely and stably lift a variety of heavy and fragile objects. The robot can also squeeze through tight quarters and push through clutter to reach and grasp a desired object.

The team envisions that this type of robot gripper could be used in a wide range of scenarios, from agricultural harvesting to loading and unloading heavy cargo. In the near term, the group is exploring applications in eldercare settings, where soft inflatable robotic vines could help to gently lift a person out of bed.

“Transferring a person out of bed is one of the most physically strenuous tasks that a caregiver carries out,” says Kentaro Barhydt, a PhD candidate in MIT’s Department of Mechanical Engineering. “This kind of robot can help relieve the caretaker, and can be gentler and more comfortable for the patient.”

Barhydt, along with his co-first author from Stanford, O. Godson Osele, and their colleagues, present the new robotic design today in the journal Science Advances. The study’s co-authors are Harry Asada, the Ford Professor of Engineering at MIT, and Allison Okamura, the Richard W. Weiland Professor of Engineering at Stanford University, along with Sreela Kodali and Cosmia du Pasquier at Stanford University, and former MIT graduate student Chase Hartquist, now at the University of Florida, Gainesville.

Open and closed

The team’s Stanford collaborators, led by Okamura, pioneered the development of soft, vine-inspired robots that grow outward from their tips. These designs are largely built from thin yet sturdy pneumatic tubes that grow and inflate with controlled air pressure. As they grow, the tubes can twist, bend, and snake their way through the environment, and squeeze through tight and cluttered spaces.

Researchers have mostly explored vine robots for use in safety inspections and search and rescue operations. But at MIT, Barhydt and Asada, whose group has developed robotic aides for the elderly, wondered whether such vine-inspired robots could address certain challenges in eldercare — specifically, the challenge of safely lifting a person out of bed. Often in nursing and rehabilitation settings, this transfer process is done with a patient lift, operated by a caretaker who must first physically move a patient onto their side, then back onto a hammock-like sheet. The caretaker straps the sheet around the patient and hooks it onto the mechanical lift, which then can gently hoist the patient out of bed, similar to suspending a hammock or sling.

The MIT and Stanford team imagined that as an alternative, a vine-like robot could gently snake under and around a patient to create its own sort of sling, without a caretaker having to physically maneuver the patient. But in order to lift the sling, the researchers realized they would have to add an element that was missing in existing vine robot designs: Essentially, they would have to close the loop.

Most vine-inspired robots are designed as “open-loop” systems, meaning they act as open-ended strings that can extend and bend in different configurations, but they are not designed to secure themselves to anything to form a closed loop. If a vine robot could be made to transform from an open loop to a closed loop, Barhydt surmised that it could make itself into a sling around the object and pull itself up, along with whatever, or whomever, it might hold.

For their new study, Barhydt, Osele, and their colleagues outline the design for a new vine-inspired robotic gripper that combines both open- and closed-loop actions. In an open-loop configuration, a robotic vine can grow and twist around an object to create a firm grasp. It can even burrow under a human lying on a bed. Once a grasp is made, the vine can continue to grow back toward and attach to its source, creating a closed loop that can then be retracted to retrieve the object.

“People might assume that in order to grab something, you just reach out and grab it,” Barhydt says. “But there are different stages, such as positioning and holding. By transforming between open and closed loops, we can achieve new levels of performance by leveraging the advantages of both forms for their respective stages.”

Gentle suspension

As a demonstration of their new open- and closed-loop concept, the team built a large-scale robotic system designed to safely lift a person up from a bed. The system comprises a set of pressurized boxes attached on either end of an overhead bar. An air pump inside the boxes slowly inflates and unfurls thin vine-like tubes that extend down toward the head and foot of a bed. The air pressure can be controlled to gently work the tubes under and around a person, before stretching back up to their respective boxes. The vines then thread through a clamping mechanism that secures the vines to each box. A winch winds the vines back up toward the boxes, gently lifting the person up in the process.

“Heavy but fragile objects, such as a human body, are difficult to grasp with the robotic hands that are available today,” Asada says. “We have developed a vine-like, growing robot gripper that can wrap around an object and suspend it gently and securely.”

"There’s an entire design space we hope this work inspires our colleagues to continue to explore,” says co-lead author Osele. “I especially look forward to the implications for patient transfer applications in health care.”

“I am very excited about future work to use robots like these for physically assisting people with mobility challenges,” adds co-author Okamura. “Soft robots can be relatively safe, low-cost, and optimally designed for specific human needs, in contrast to other approaches like humanoid robots.”

While the team’s design was motivated by challenges in eldercare, the researchers realized the new design could also be adapted to perform other grasping tasks. In addition to their large-scale system, they have built a smaller version that can attach to a commercial robotic arm. With this version, the team has shown that the vine robot can grasp and lift a variety of heavy and fragile objects, including a watermelon, a glass vase, a kettle bell, a stack of metal rods, and a playground ball. The vines can also snake through a cluttered bin to pull out a desired object.

“We think this kind of robot design can be adapted to many applications,” Barhydt says. “We are also thinking about applying this to heavy industry, and things like automating the operation of cranes at ports and warehouses.”

This work was supported, in part, by the National Science Foundation and the Ford Foundation.

Lewis, the Hansjörg Wyss Professor of Biologically Inspired Engineering at Harvard University, is pursuing that challenge through advances in 3D printing. In her talk presented to a combined in-person and virtual audience of over 500 attendees, Lewis shared work from her lab that focuses on enhanced function in 3D printed components for use in soft electronics, robotics, and life sciences.

“How you make a material affects its structure, and it affects its properties,” said Lewis. “This perspective was a light bulb moment for me, to think about 3D printing beyond just prototyping and making shapes, but really being able to control local composition, structure, and properties across multiple scales.”

A trained materials scientist, Lewis reflected on learning to speak the language of biologists when she joined Harvard to start her own lab focused on bioprinting and biological engineering. How does one compare particles and polymers to stem cells and extracellular matrices? A key commonality, she explained, is the need for a material that can be embedded and then erased, leaving behind open channels. To meet this need, Lewis’ lab developed new 3D printing methods, sophisticated printhead designs, and viscoelastic inks — meaning the ink can go back and forth between liquid and solid form.

Displaying a video of a moving robot octopus named Octobot, Lewis showed how her group engineered two sacrificial inks that change from fluid to solid upon either warming or cooling. The concept draws inspiration from nature — plants that dynamically change in response to touch, light, heat, and hydration. For Octobot, Lewis’ team used sacrificial ink and an embedded printing process that enables free-form printing in three dimensions, rather than layer-by-layer, to create a fully soft autonomous robot. An oscillating circuit in the center guides the fuel (hydrogen peroxide), making the arms move up and down as they inflate and deflate.

From robots to whole organ engineering

“How can we leverage shape morphing in tissue engineering?” asked Lewis. “Just like our blood continuously flows through our body, we could have continuous supply of healing.”

Lewis’ lab is now working on building human tissues, primarily cardiac, kidney, and cerebral tissue, using patient-specific cells. The motivation, Lewis explained, is not only the need for human organs for people with diseases, but the fact that receiving a donated organ means taking immunosuppressants the rest of your life. If, instead, the tissue could be made from your own cells, it would be a stronger match to your own body.

“Just like we did to engineer viscoelastic matrices for embedded printing of functional and structural materials,” said Lewis, “we can take stem cells and then use our sacrificial writing method to write in perfusable vasculature.” The process uses a technique Lewis calls SWIFT — sacrificial writing into functional tissue. Sharing lab results, Lewis showed how the stem cells, differentiated into cardiac building blocks, are initially beating individually, but after being packed into a tighter space that will support SWIFT, these building blocks fuse together and become one tissue that beats synchronously. Then, her team uses a gelatin ink that solidifies or liquefies with temperature changes to print the complex design of human vessels, flushing away the ink to leave behind open lumens. The channel remains open, mimicking a blood vessel network that could have fluid actively, continuously flowing through it. “Where we’re going is to expand this not only to different tissue types, but also building in mechanisms by which we can build multi-scale vasculature,” said Lewis.

Honoring Mildred S. Dresselhaus

In closing, Lewis reflected on Dresselhaus’ positive impact on her own career. “I want to dedicate this [talk] to Millie Dresselhaus,” said Lewis. She pointed to a quote by Millie: “The best thing about having a lady professor on campus is that it tells women students that they can do it, too.” Lewis, who arrived at MIT as a materials science and engineering graduate student in the late 1980s, a time when there were very few women with engineering doctorates, noted that “just seeing someone of her stature was really an inspiration for me. I thank her very much for all that she’s done, for her amazing inspiration both as a student, as a faculty member, and even now, today.”

After the lecture, Lewis was joined by Ritu Raman, the Eugene Bell Career Development Assistant Professor of Tissue Engineering in the MIT Department of Mechanical Engineering, for a question-and-answer session. Their discussion included ideas on 3D printing hardware and software, tissue repair and regeneration, and bioprinting in space. 

“Both Mildred Dresselhaus and Jennifer Lewis have made incredible contributions to science and served as inspiring role models to many in the MIT community and beyond, including myself,” said Raman. “In my own career as a tissue engineer, the tools and techniques developed by Professor Lewis and her team have critically informed and enabled the research my lab is pursuing.”

This was the seventh Dresselhaus Lecture, named in honor of the late MIT Institute Professor Mildred Dresselhaus, known to many as the "Queen of Carbon Science.” The annual event honors a significant figure in science and engineering from anywhere in the world whose leadership and impact echo Dresselhaus’ life, accomplishments, and values. 

“Professor Lewis exemplifies, in so many ways, the spirit of Millie Dresselhaus,” said MIT.nano Director Vladimir Bulović. “Millie’s groundbreaking work, indeed, is well known; and the groundbreaking work of Professor Lewis in 3D printing and bio-inspired materials continues that legacy.”

Qualcomm’s unveiling of the Dragonwing IQ‑10, its first dedicated robotics processor, marks a pivotal moment in the evolution of “physical AI”—the fusion of embodied robotics with advanced machine learning. Presented at the India AI Impact Summit 2026, the platform showcases a full-stack robotics architecture designed to accelerate humanoid and autonomous mobile robot deployment across factories, warehouses, and even homes.

What makes this announcement more than another incremental hardware upgrade is Qualcomm’s explicit ambition: to create a general-purpose robotics foundation capable of continuous learning, multimodal perception, and industrial‑grade reliability. The company’s modular architecture—combining edge compute, ML operations, and an “AI data flywheel”—positions robots not as static machines but as adaptive agents capable of improving autonomously in real‑world environments.

For the AI Quantum Intelligence community, this signals a deeper shift. We’re moving from AI as a purely digital intelligence to AI as a physical force multiplier—a transition that mirrors the leap from classical computation to quantum‑accelerated problem solving. The Dragonwing IQ‑10 isn’t just a chip; it’s a declaration that embodied AI is ready to scale and that nations like India are emerging as strategic hubs for robotics innovation.

The implications are profound. As physical AI systems become more capable, industries will reorganize around autonomous workflows, hybrid human‑robot teams, and continuous learning loops that blur the line between software updates and mechanical evolution. The next competitive frontier won’t be who has the best model—it will be who can deploy intelligent machines fastest, safest, and at industrial scale.

Qualcomm’s move suggests that the era of humanoids and AMRs as niche prototypes is ending. The era of physical AI infrastructure—globally distributed, continuously learning, and economically transformative—is beginning.

Written/published by AI Quantum Intelligence with the help of AI models.

Sources:

Beyond the Screen: Qualcomm Debuts ‘Physical AI’ Humanoids in New Delhi to Revolutionize Indian Factories

From AMRs to humanoids: Qualcomm showcases full robotics suite at India AI Impact Summit 2026 | Mint

Article Summary

The article addresses a critical transition in the robotics industry: the movement of robots from the controlled environments of industrial assembly lines (Industry 3.0) into the complex, unpredictable fabric of human society (Industry 4.0 and beyond). Morasso argues that for robots to become truly "civilized" and socially acceptable, they must move beyond being mere "super-intelligent" tools driven by Large Language Models (LLMs) or Embodied Artificial Intelligence (EAI).

Key highlights include:

• The Roadmap Debate: The author compares two competing paradigms for robotic development:

  1. EAI (Embodied Artificial Intelligence): Relying on massive foundation models to dictate behaviour.

  2. Bio-inspired Cognitive Robotics: Architectures based on embodied cognition, developmental psychology, and social interaction.

• Ethical Autonomy: Morasso suggests that current foundation models are "ethically agnostic" because they are intrinsically disembodied. He proposes that true "wisdom"—the ability to make right-vs-wrong decisions in conflict situations—requires a bio-inspired cognitive architecture that learns through physical and social development rather than just data ingestion.

• Computational Frugality: The paper advocates for "computational frugality," emphasizing that effective human-robot interaction (HRI) should be based on enaction theory and ecological psychology rather than the energy-intensive scaling of current AI models.

Our Opinion: The Quest for the "Civilized" Machine

The narrative presented by Morasso is both timely and technically provocative. In terms of completeness, the article successfully bridges the gap between abstract AI ethics and practical robotic control. It correctly identifies the "cognitive interaction" hurdle—where a robot’s goals might conflict with a human's—as a much larger challenge than simple physical safety. By contrasting the popular "Embodied AI" trend (driven by companies like Tesla and Figure) with "Bio-inspired Cognitive Robotics," the article provides a necessary reality check for enthusiasts who believe that scaling LLMs is the sole path to AGI.

However, the narrative could be considered slightly incomplete regarding the immediate hardware limitations. While the author focuses on the "brain" of the robot, the physical "body" (actuators, battery density, and haptic sensors) remains a significant bottleneck that the article touches upon only briefly. Furthermore, the correctness of the "wisdom" hypothesis—that robots must evolve like children to be ethical—is an ongoing debate. Critics might argue that "guardrail" programming or alignment tuning in foundation models could achieve social acceptability much faster than long-term developmental learning. Nevertheless, for readers of AI Quantum Intelligence, this article serves as a crucial foundational text for understanding the next decade of "civilized" robotics.

Direct Source Link: Bio-inspired cognitive robotics vs. embodied AI for socially acceptable, civilized robots

References

Morasso, P. (2026). Bio-inspired cognitive robotics vs. embodied AI for socially acceptable, civilized robots. Frontiers in Robotics and AI, 12. https://doi.org/10.3389/frobt.2026.1714310

Written/published by AI Quantum Intelligence with the help of AI models.

In the relentless pursuit of smarter machines, the spotlight has often been on large, cloud-based AI models, dazzling us with their ability to generate text, images, and complex analyses. Yet, as we push towards truly autonomous systems that interact with the physical world – from nimble robots navigating dynamic factory floors to intelligent sensors monitoring critical infrastructure – a fundamental challenge emerges: the need for intelligence that operates not just in the cloud, but robustly at the edge.

The aspiration for Artificial Intelligence has always been to equip machines with human-like adaptability. Traditional AI, while powerful, often struggles with the unpredictable nuances of real-world environments. It's often rigid, slow to react, and heavily reliant on vast data centers. This paradigm is simply unsustainable for the next generation of robotics and the expanding Internet of Things (IoT). What if our devices could learn and adapt in real-time, on-device, with minimal power consumption and maximum resilience?

This article explores a pivotal convergence point addressing this exact challenge: the synergistic integration of Edge AI and a revolutionary class of algorithms known as Liquid Neural Networks (LNNs). This powerful combination is poised to transform robotics and IoT, ushering in an era of truly adaptive, efficient, and resilient autonomous systems. We'll delve into what makes this convergence so significant, illustrate its impact with specific examples, and look ahead at the profound implications for our increasingly connected and automated world.

The Rise of Edge AI: Bringing Intelligence to the Source

Edge AI refers to the deployment of AI models directly on local devices (the "edge" of the network) rather than relying on a centralized cloud server. This paradigm shift is driven by several critical factors:

1. Latency Reduction: For applications like autonomous vehicles or robotic surgery, milliseconds matter. Processing data locally eliminates the round-trip delay to the cloud, enabling instantaneous decision-making. Imagine a drone needing to instantly adjust its flight path to avoid an unexpected obstacle – cloud dependency simply isn't an option.
2. Bandwidth Conservation: As the number of IoT devices explodes, sending all raw sensor data to the cloud becomes prohibitively expensive and strains network infrastructure. Edge AI processes data locally, sending only relevant insights or anomalies upstream, drastically reducing bandwidth usage. Consider thousands of smart city sensors; only critical events need to be communicated.
3. Enhanced Privacy and Security: Processing sensitive data (e.g., medical devices, surveillance cameras) locally minimizes its exposure to potential breaches during transmission or storage in the cloud.
4. Offline Operation: Edge AI enables devices to function effectively even when disconnected from the internet, crucial for remote sensing in agriculture, disaster response, or deep-sea exploration.
5. Cost Efficiency: Reducing reliance on continuous cloud processing and storage can lead to significant operational cost savings over time.

While the benefits are clear, traditional deep learning models, optimized for powerful cloud GPUs, are often too large and power-hungry for typical edge devices with limited computational resources, memory, and battery life. This is where Liquid Neural Networks enter the picture.

Liquid Neural Networks: Biology-Inspired Adaptability

Developed by researchers at MIT, Liquid Neural Networks (LNNs) represent a radical departure from conventional neural network architectures. Unlike static models that are 'frozen' after training, LNNs are designed to be continuously adaptive, processing information like biological brains.

The key characteristics that make LNNs game-changers for Edge AI, Robotics, and IoT include:

• Continuous-Time Operation: LNNs are defined by continuous differential equations, allowing them to process time-series data (like sensor readings) with remarkable precision and adapt their internal state in a fluid, continuous manner. This mimics the dynamic processing found in biological neurons.
• Extreme Compactness and Efficiency: Despite their sophisticated dynamics, LNNs can be incredibly small – often requiring orders of magnitude fewer neurons and parameters than traditional networks to achieve comparable or superior performance, especially on sequential tasks. This makes them ideal for resource-constrained edge devices.
• Enhanced Robustness and Interpretability: Their dynamic nature allows LNNs to handle noise, missing data, and unexpected inputs more gracefully. Furthermore, their simpler, biologically inspired structure can often make their decision-making processes more transparent and interpretable, a critical feature for safety-critical applications in robotics.
• Real-time Adaptability: This is perhaps their most defining feature. LNNs are not just good at learning from historical data; they can continue to learn and adapt to new, unforeseen conditions after deployment without requiring a complete retraining cycle or cloud access.

Example: Imagine a traditional neural network trained to classify objects in a specific lighting condition. If the lighting changes, its performance degrades. An LNN, however, could be designed to dynamically adjust its internal parameters to compensate for the new lighting, continuously optimizing its performance in real-time without external intervention.

The Synergistic Convergence: Edge AI Meets LNNs

The fusion of Edge AI and Liquid Neural Networks creates a powerful synergy that addresses the limitations of each approach individually:

1. Enabling True On-Device Learning and Adaptation: Edge devices powered by LNNs can learn from their local environment and refine their behavior continuously. This moves beyond merely deploying intelligence to fostering intelligence directly on the machine.
2. Unprecedented Efficiency for Resource-Constrained Devices: LNNs' compact size and low computational demands make them a perfect fit for tiny microcontrollers and sensors in IoT networks, enabling sophisticated intelligence where it was previously impossible.
3. Robustness in Dynamic, Unpredictable Environments: Robotics operating in the real world constantly encounter novel situations. LNNs provide the inherent adaptability to handle these challenges, making robots more resilient and less prone to failure in complex scenarios.
4. Faster Development Cycles: The ability of LNNs to adapt post-deployment means less rigid initial training requirements and faster iteration in development, as models can fine-tune themselves in the field.

Specific Examples and Use Cases:

The practical implications of this convergence are vast and transformative across multiple sectors:

• Autonomous Robotics (Robotics):
• Industrial Collaborative Robots (Cobots): LNN-powered cobots could learn the subtle motion patterns of human co-workers in real-time, adapting their own movements to optimize safety and efficiency without needing reprogramming for every new task variation or environmental change.
• Search and Rescue Drones: Drones equipped with LNNs could navigate collapsed buildings or dense forests, dynamically adjusting flight paths and sensor interpretation to account for evolving debris fields, smoke, or unexpected obstacles, all while operating entirely offline. They could learn optimal search patterns based on the immediate environment's characteristics.
• Smart Infrastructure & Predictive Maintenance (IoT):
• Bridge Health Monitoring: IoT sensors embedded in bridges could use LNNs to continuously learn the structural dynamics. Rather than just reporting raw vibration data, an LNN could adapt its understanding of "normal" behavior as the bridge ages or experiences various traffic loads, detecting subtle anomalies that indicate early signs of fatigue or damage with greater accuracy and fewer false positives, directly on the sensor itself.
• Precision Agriculture: Agricultural robots or smart irrigation systems could use LNNs to monitor soil conditions, plant health, and local weather patterns. They could adapt water delivery or nutrient application strategies based on real-time, hyper-local conditions, learning from each crop cycle to optimize yields and resource usage.
• Personalized Healthcare (IoT/Edge AI):
• Wearable Medical Devices: Future smartwatches or continuous glucose monitors could use LNNs to learn an individual's unique physiological responses, continuously refining their predictions and alerts based on personal data streams (sleep, diet, activity) without sending sensitive health data to the cloud. This would lead to highly personalized and proactive health insights.
• Adaptive Security Systems (Edge AI/IoT):
• Intelligent Surveillance Cameras: Cameras with LNNs could learn "normal" behavioral patterns within a specific environment (e.g., a retail store, a public square). They could then adapt to subtle changes, identifying anomalous behavior more effectively, reducing false alarms, and requiring less human oversight for security monitoring. The system learns what should be happening rather than relying on predefined rules.

Conclusion: The Dawn of Truly Living Intelligence

The convergence of Edge AI and Liquid Neural Networks marks a significant leap towards creating machines that are not just intelligent, but truly adaptive and resilient. It heralds a future where AI is not confined to remote data centers but is woven directly into the fabric of our physical world, enabling devices to learn, evolve, and thrive in dynamic environments.

In the next one to five years, we can anticipate several key developments:

• Increased Commercialization: Expect to see LNN-powered chips and software libraries become more widely available, accelerating their integration into commercial robotics and IoT products. Startups specializing in compact, adaptive AI will emerge as key players.
• Standardization of Edge ML Frameworks: As this field matures, there will be greater standardization in frameworks and toolchains for deploying and managing LNNs on edge devices, making it easier for developers to leverage this technology.
• Breakthroughs in Energy Harvesting and TinyML: The efficiency of LNNs will further drive innovations in ultra-low-power computing and energy harvesting, enabling devices with intelligence that can operate for extended periods, or even indefinitely, without external power.
• Heightened Ethical Considerations: As robots and IoT devices become more autonomous and adaptive, the ethical implications surrounding their decision-making, data privacy, and potential for unintended consequences will become even more pressing and require robust regulatory frameworks.

The journey towards ubiquitous, adaptive intelligence is well underway. Edge AI and Liquid Neural Networks are not merely incremental improvements; they represent a fundamental shift in how we conceive and deploy AI, moving us closer to a future where machines truly understand, adapt to, and augment the complexities of the human experience.

We'd love to hear your thoughts! What applications of adaptive Edge AI and Liquid Neural Networks excite you the most? Do you foresee any specific challenges in their widespread adoption? Share your insights and comments below!

Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence).

For decades, robotics has been synonymous with industrial arms, surgical assistants, and automated manufacturing lines. But a quieter revolution is happening in living rooms, backyards, and hobbyist workshops. The personal and home robotics market—once a niche curiosity—is now a rapidly expanding sector projected to reach USD 28.1 billion by 2033 and driven by consumer demand for convenience, independence, and playful innovation.

This article explores the robots already shaping everyday life, the emerging concepts in development, and the best DIY kits empowering creators to build their own practical machines.

Robots Already in the Home: What’s Available Today

A. Cleaning & Maintenance Robots

These represent the most mature and widely adopted category.

·        Robotic Vacuum Cleaners
The flagship of home robotics. Devices like Roomba, Roborock, and Dyson’s prototypes dominate a market expected to exceed $5 billion by 2025.
Capabilities now include:

o   AI‑based room mapping

o   Obstacle recognition

o   Self‑emptying bins

o   Combined vacuum/mop systems

·        Floor Washing & Mopping Robots
Brands like iRobot Braava and Narwal offer autonomous wet cleaning with advanced navigation.

·        Lawn‑Mowing Robots
Husqvarna, Worx, and Segway have created fully autonomous mowers that maintain lawns with GPS, perimeter wires, or vision‑based navigation.

·        Pool‑Cleaning Robots
Dolphin and Aiper produce underwater robots that scrub pool floors and walls.

These categories form the backbone of the home maintenance robotics market, valued at USD 2.5 billion in 2024 and projected to triple by 2033.

B. Personal Assistance & Companion Robots

Elder Care & Wellness Robots

Driven by aging populations and rising healthcare costs, this segment is accelerating.

·        ElliQ
An AI‑driven companion robot designed to support older adults with reminders, wellness check‑ins, and social engagement. It’s already being deployed in U.S. counties through public programs.

·        Temi Personal Robot
A mobile assistant that can follow users, make video calls, and integrate with smart home systems.

·        Robotic Mobility Aids
Early prototypes include robotic walkers, exoskeleton‑assisted mobility devices, and fall‑detection robots.

C. Social & Entertainment Robots

While the first wave (e.g., Jibo, Anki Vector) struggled commercially, the category is resurging.

·        Moxie by Embodied
A social robot designed for children’s emotional development.

·        Loona by KEYi Tech
A highly expressive pet‑like robot with advanced locomotion and personality‑driven interactions.

·        Sony Aibo
A premium robotic dog with lifelike behavior and cloud‑based learning.

D. Recreational, Educational & Hobbyist Robots

STEM & Learning Robots

These are designed for experimentation, coding, and creativity.

·        Sphero BOLT & RVR
Programmable robots for learning robotics fundamentals.

·        LEGO Mindstorms / LEGO SPIKE Prime
A classic platform for building functional robots with modular components.

·        Makeblock mBot & Ultimate Kit
Arduino‑based educational robots with strong expandability.

E. Consumer Drones

While not always labeled as “robots,” drones are autonomous robotic systems.

·        DJI Mini, Air, and FPV series

·        Skydio autonomous drones (notable for AI‑driven obstacle avoidance)

2. Robots in Development: What’s Coming Next

A. Advanced Home Assistants

Companies like Dyson are investing heavily in home robots capable of:

·        Picking up clutter

·        Loading dishwashers

·        Folding laundry

·        Manipulating household objects

Dyson has publicly revealed prototypes and is building the UK’s largest robotics research facility.

B. Kitchen Robotics

Startups are developing:

·        Robotic chefs

·        Automated bartenders

·        Smart robotic appliances (e.g., chopping, stirring, dispensing)

C. Personal Mobility & Delivery Robots

Expect to see:

·        Indoor delivery bots for multi‑floor homes

·        Personal “butler” robots

·        Autonomous shopping carts

D. Emotional & Cognitive Companion Robots

Next‑gen models will integrate:

·        Large‑language‑model reasoning

·        Adaptive personalities

·        Long‑term memory

·        Multi‑modal perception

Best DIY Robotics Kits for Builders & Innovators

For creators perhaps like you—who blend technical rigor with creative experimentation—these kits offer the best balance of power, flexibility, and practical utility.

A. Arduino‑Based Kits

Perfect for hands‑on builders who want full control.

B. Raspberry Pi‑Based Kits

Ideal for AI, vision, and networking.

C. Modular Robotics Platforms

For rapid prototyping and creative exploration.

·        VEX Robotics V5
High‑end educational system with metal components.

·        Trossen Robotics PhantomX Series
Humanoid and quadruped kits with ROS support.

·        Open‑Source ROS Robots (TurtleBot 4, Husarion ROSbot)
Ideal for SLAM, navigation, and AI robotics research.

D. 3D‑Printed & Customizable Platforms

For builders who want to design from scratch.

·        OpenDog / OpenQuadruped projects

·        OpenManipulator

·        DIY drone frames + Pixhawk flight controllers

Market Trends Driving Personal Robotics Forward

Based on an analysis of robotics for the home, personal and leisure markets:

• The personal and homecare robotics market is growing at 15.55% CAGR through 2033.
• The home maintenance robotics segment is expanding rapidly due to smart home adoption and demand for convenience.
• Household robots are projected to reach USD 24.05 billion by 2030.
• Aging populations and wellness initiatives are accelerating demand for companion and eldercare robots.

In short: the personal robotics market is no longer a novelty—it’s becoming a core part of modern living.

Final Thoughts: A Market on the Edge of Explosion

Personal robotics is transitioning from “cute gadgets” to functional household infrastructure. Cleaning robots paved the way, but the next decade will bring:

• Robots that tidy, cook, and organize
• Robots that support aging populations
• Robots that entertain, teach, and collaborate
• Robots that hobbyists can build, customize, and deploy

Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence).

1. What’s the real cost of palletizing automation?

The cost of automation isn’t just the hardware; it’s a strategic investment in efficiency, consistency, and scalability. Key cost considerations include:

• Cobot arm: The main hardware.
• End-effector: Grippers or suction cups for handling products.
• Integration & setup: Conveyor adjustments, electrical work, and line modifications.
• Software & programming: Intuitive or custom interfaces to run your application.
• Custom engineering: Complex projects may require specialized tooling or expert designers, increasing costs.
  
  

Lean Palletizing Advantage:
PAL Ready is production-ready on day one, with smart infeed, mobility features, built-in safety scanners, and adaptive grippers—delivering fast ROI without the headaches of engineering or programming delays. PAL Series provides modular scalability for growing operations.

ROI Example: Most Lean Palletizing customers recover their investment in 1–2 years through reduced setup time, consistent uptime, and simplified maintenance. Glenhaven Foods reduced operator strain, maintained consistent throughput, and achieved ROI in just 13 months, while employees could focus on higher-value tasks.

2. How do I fit a palletizer into a real-world factory?

Space is often the biggest barrier to automation. Many facilities weren’t designed for robotics, which is why compact solutions like PAL Ready matter:

• Footprint: About half a pallet: it fits into tight lines without reconfiguring your floor.
• Modular & mobile: Can be relocated between lines or seasonal setups.
• Safe & accessible: Built-in safety scanners maintain visibility and workflow efficiency.
• Pre-assembled: Deploy on day one with minimal installation effort.

Example: Panovo, a snack and frozen bakery producer, deployed five PAL Series cells in limited space. They achieved reliable, consistent stacking, even in low-temperature environments, without expanding their footprint.

3. Which palletizer is right for my factory? 

There’s no one-size-fits-all answer. The best solution depends on your throughput, space, budget, and flexibility needs. Here’s a quick overview:

• Engineered centralized palletizers: High-volume, multi-line facilities; large footprint; long ROI; 30–80 cases/min.
• Engineered end-of-line robotic palletizers: High-capacity single lines; durable and reliable; 25–60 cases/min.
• Engineered end-of-line cobot palletizers: Flexible, space-efficient; safe for operators; 8–15 cases/min
• Plug-and-play “In-a-Box” palletizers: Compact, fast installation; moderate throughput; limited customization.
• Lean Palletizing (PAL Ready / PAL Series): Mid-volume, high-mix operations; quick deployment; scalable; predictable 1–2 year ROI.
  
  

Key factors to consider: throughput, available floor space, budget, frequency of SKU changes, and safety/ergonomics. Tools like the Robotiq Palletizing Fit Tool can help simulate your cell, calculate ROI, and plan your setup.

4. Can automation improve safety while maintaining productivity?

Yes. Manual palletizing carries high ergonomic risks: lifting, bending, twisting, and repetitive motions can lead to injuries, fatigue, and downtime. Collaborative cobots like PAL Ready are designed to work safely alongside humans:

• Built for collaboration: Force and speed limits, contact sensors, and rounded edges protect operators.
• Compact and contained: Clearly defined operational zone reduces hazards.
• Smart controls: Predictable motion, pre-programmed recipes, intuitive interface.
• Compliance: Meets ISO/TS 15066 standards for collaborative operation.
  
  

Real-world impact: At RAIN Pure Mountain Spring Water, operators were lifting 30 lb boxes in a compact facility, causing fatigue and turnover risk. After implementing Lean Palletizing, cobots handled the heavy lifting, while operators focused on higher-value tasks. Throughput increased, ergonomic risks decreased, and team satisfaction improved.

5. How can I scale and future-proof my palletizing operations?

Flexibility and scalability are key. Lean Palletizing solutions are designed for growing operations:

• PAL Ready: Pre-assembled, high-mix palletizing in a compact footprint. Ideal for fast deployment and seasonal/contract lines.
• PAL Series: Modular, configurable, and scalable to multi-line or multi-site setups.
• Smart software: Material Handling Copilot allows operators to run multiple SKUs without programming expertise.
• ROI-focused: Fast deployment, reduced downtime, and predictable payback periods make scaling simpler and more profitable.
  
  

By standardizing on adaptive cobot hardware and intuitive software, manufacturers can replicate successful cells across lines, reduce integration time, and avoid costly custom tooling.

Take the next step

Choosing the right palletizer doesn't have to be guesswork. With the Robotiq Palletizing Fit Tool, you can:

• Validate your application in minutes
• Generate a custom 3D simulation of your palletizing cell
• Calculate ROI and payback period with your real production data
• Receive a detailed report to share with your team
  
  

Whether you want to protect your workforce, maximize throughput, or scale with confidence, Lean Palletizing offers a practical, factory-friendly approach to end-of-line automation.

Across food and beverage, pharma, and consumer goods, our palletizing deployments have taught us a lot about what separates a “successful install” from a solution that keeps paying off year after year.

Here are five lessons that show up again and again and what they might mean for your own end-of-line.

1. The smoothest deployments start long before installation day

The fastest palletizing projects aren’t the ones with the simplest lines; they’re the ones where the application fit got nailed early.

Before any robot shows up, the most successful deployments take time to confirm:

• Box stability and consistency (weight, rigidity, surface, condition)
• Throughput targets (what the line actually runs today vs. what it should run tomorrow)
• SKU variability (number of patterns, case sizes, changeover expectations)
• Real end-of-line constraints (space, pallet access, infeed height, floor quality)
  
  

This is why we built tools and processes to qualify applications upfront. When the fit is right at the start, install goes faster, tuning goes smoother, and expectations are aligned from day one. That front-end clarity saves weeks later.

Takeaway: The best ROI doesn’t start with the robot. It starts with the right application decision.

2. Time-to-value beats “perfect on paper” specs

In the real world, customers don’t measure success by max payload or theoretical cycle time. They measure it by how quickly the palletizer becomes useful.

Across our deployments, the biggest wins come from:

• Short installation windows
  
• Fast ramp-up to production
  
• Simple handoff to operators
  
• A clear path from “first pallet” to “steady state”
  
  

Most of our palletizing cells are installed and operational in just a few days — because what matters most is getting value on the floor quickly, not chasing an idealized setup.

That doesn’t mean ignoring performance. It means prioritizing what accelerates real production:

• the right layout
• a stable pick
• a safe, predictable flow
• and an installation process built for factories, not lab demos

Takeaway: The quicker a system creates value, the more momentum it has inside the plant.

3. Adoption succeeds when operators feel ownership 

One of the most consistent truths we’ve seen: automation works best when operators take it personally.

The deployments that scale and stick almost always share a similar pattern:

• one or two operators become solution champions
• they learn the interface quickly
• they start making small adjustments on their own
• and soon the robot feels like “our tool,” not “their machine”

That ownership changes everything:

• issues get spotted early
• changeovers get faster with practice
• trust shows up in daily usage
• and teams start looking for where else automation can help
  
  

We’ve watched plants go from skepticism to pride in weeks — not because the robot is flashy, but because people feel confident running it.

Takeaway: A palletizer isn’t just a machine install. It’s a confidence install.

4. Flexibility is the value customers discover after go-live

Many deployments begin with a single goal:

“We just want to palletize this one line.”

But once the palletizer is running and the team is comfortable, something predictable happens:

• new SKUs get added
• more patterns get tried
• adjacent lines get considered
• usage expands quietly month by month
  
  

Flexibility turns out to be a sleeper feature. Customers don’t always buy for it — but they love it once they have it.

We routinely see plants start with one stable product, then scale to:

• multiple case sizes
• more mixed SKU schedules
• seasonal peaks
• or even second shifts without hiring stress

That phased growth reduces risk and spreads investment over time — which is exactly what Lean factories want.

Takeaway: The best deployments don’t just solve today’s problem. They unlock tomorrow’s options.

5. Real factories are messy. Design for reality, not perfection

You’ll never find a factory that matches the CAD drawing.

Some of our most useful lessons come from the “last 10%” — the moments where floor-level reality shows up:

• product variability
• damaged cartons
• wet cases
• pallet quality differences
• uneven floors
• unexpected line surges
  
  

The good news is: these are solvable. But they need to be expected.

The deployments that succeed fastest are designed to handle reality:

• grippers chosen for actual case conditions
  
• pallet patterns built for true box behavior
  
• layouts that allow real operator access
  
• and commissioning that includes fine-tuning on the floor
  

When we plan for messiness, uptime increases, and teams stop feeling like they’re babysitting the system.

Takeaway: A palletizer doesn’t need a perfect factory. It needs a factory-ready plan.

What this means if you’re considering palletizing automation

If there’s one thing 850+ deployments have made clear, it’s this:

Palletizing automation works when it fits the real world — your products, your line, your people, and your pace.

It doesn’t require a giant redesign.
It doesn’t require perfect cases.
And it definitely doesn’t require choosing between speed and simplicity.

It requires:

1. the right application fit
2. a fast path to value
3. operator ownership
4. room to grow
5. and a design that expects reality

If you’re thinking about automation at end-of-line, start by pressure-testing the fit. A clear, early assessment makes everything easier downstream — from layout to ROI.

Curious if your line is a strong match?
Try the Palletizing Fit Tool or talk to our team for a quick evaluation. In a short session, we can validate feasibility, estimate ROI, and map a realistic deployment path.

Because after 850+ installs, we’ve learned this too: the best automation decisions are the ones you can make with confidence.

 

The challenge: Heavy cartons, weak boxes, and limited space

Eckerts produces and ships a wide range of spirits. The end-of-line palletizing process created several bottlenecks:

• Heavy cartons (up to 8 kg each) slowed down manual handling.
• Structurally weak boxes required gentle, consistent placement.
• A compact workshop made it difficult to install fully enclosed systems.
• Safety concerns arose because employees needed to work close to the robot.
• Training time for new technology had to remain minimal to avoid disruptions.

The team wanted automation that improved efficiency while protecting both product quality and employee well-being.

Why Eckerts selected Robotiq

To validate the solution, Eckerts sent its heaviest cartons to Robotiq’s Lyon office. The palletizer successfully handled every box type, demonstrating its capability before installation.

Key attributes that supported the decision:

• Open, enclosure-free layout that fit the distillery’s limited floor space.
• Integrated safety features that stop the robot automatically when people approach.
• Fast onboarding, allowing employees to use the system confidently after one day of training.
• Compatibility with 15 different carton types, reducing changeover effort. 

“This solution is one of the best things we have bought in recent years. The system has been running flawlessly from day one.”

- Production Manager Andreas Klemp

The solution: Robotiq Lean Palletizing

Robotiq Lean Palletizing provided the following capabilities:

• High throughput, supporting up to 500 cartons per hour.
• Precise handling, ideal for fragile or weaker cardboard structures.
• Safe human-robot interaction, suited for small production spaces.
• User-friendly software that simplifies setup and operation.

The system’s design allowed Eckerts to automate palletizing while preserving the workflow of a traditional distillery environment.

Results: Efficiency, safety, and faster operations

After implementing the Robotiq solution, Eckerts observed several improvements:

1. Increased efficiency

The palletizer handled heavy loads consistently, reducing manual lifting and improving overall throughput.

2. Lower labor costs

Operators could shift from repetitive tasks to higher-value responsibilities in production.

3. Safer working conditions 

Built-in safety features maintained a secure environment, even within a compact workspace.

4. Rapid employee adoption 

Training required only one day, reducing downtime and enabling immediate use.

The impact: Tradition supported by modern automation

Eckerts’ experience demonstrates how automation can integrate seamlessly into a heritage-focused industry. Robotiq Lean Palletizing helped the distillery:

• Maintain product quality
• Reduce physical strain
• Improve production stability
• Support long-term operational sustainability

Automation became a way to protect craftsmanship, not replace it.

Explore Lean Palletizing for yourself 

Manufacturers facing heavy loads, mixed packaging, or workforce constraints can benefit from the same reliable and safe automation system used at Eckerts.

If you want to see whether palletizing automation makes sense for your facility, start with the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

Want more stories from real factories like yours?

Follow Robotiq on LinkedIn and join over 75,000 manufacturers seeing how automation keeps people safe, and production running strong.

Across surveys and what we see on real floors, the pattern is clear: manufacturers are prioritizing automation that helps them stabilize output, protect people, and stay flexible under uncertainty. Persistent labor shortages and skills gaps are still the backdrop, so the “why” isn’t mysterious; it’s operational survival and competitive advantage. (Deloitte)

Here’s where automation is concentrating in 2026, and the reasons behind each move.

1. End-of-line (palletizing, case packing, warehousing handoff)

What’s getting automated: palletizing, depalletizing, case handling, stretch wrapping, and other end-of-line tasks.

Why in 2026:

• Labor relief where the pain is constant. End-of-line roles are physically demanding and hard to staff consistently. With skilled labor shortages still acute, these stations are prime targets for robotics. (Deloitte)
  
  
• Clear ROI and fast time-to-value. These applications have measurable throughput and safety gains, making the business case easier than many upstream processes.
  
  
• Flexibility is finally good enough. Plants want automation that can handle SKU churn and seasonal peaks. “Hyper-flexible” manufacturing — systems that can be reconfigured quickly — is a defining shift right now. (The Economic Times)
  
  

Robotiq lens: End-of-line is often the first automation step because it’s the cleanest win: stable process, visible impact, and low disruption to upstream production.

2. Intralogistics and material movement

What’s getting automated: AMRs/AGVs for moving pallets, totes, and WIP; automated line feeding; buffer management.

Why in 2026:

• Flow beats speed. Manufacturers are realizing that internal transport bottlenecks quietly kill OEE. Automating movement creates smoother flow without redesigning the whole plant.
  
  
• Easier to scale than fixed conveyors. Mobile robotics aligns with the shift to modular, software-defined factories where layouts change more often. (The Economic Times)

Safety + predictability. Automated transport reduces forklift traffic and “last-minute heroics.”

3. Inspection and quality control 

What’s getting automated: vision inspection, in-process measurement, AI-assisted defect detection.

Why in 2026:

• Quality is a top digital investment area. Surveys show quality management and AI-enabled inspection sit near the top of manufacturers’ smart-tech spend. (Rockwell Automation)
  
  
• AI makes variability manageable. Modern vision + ML handles imperfect real-world variation better than traditional rules-based QC.

Scrap costs are too high to ignore. Catching issues earlier has a compounding effect on yield and scheduling reliability.

4. Machine tending and repetitive production tasks 

What’s getting automated: CNC/press tending, loading/unloading, simple pick-and-place, packaging steps, secondary ops.

Why in 2026:

• High repeatability = quick wins. These tasks are stable, easy to standardize, and ideal for cobots and compact cells.
  
  
• Aging skilled trades gap. The shortage isn’t just general labor; it’s experienced operators and technicians. Automation helps keep machines running even when staffing is thin. (Deloitte)

Plants are scaling beyond pilots. Many manufacturers are moving from experiments to deployments that deliver measurable output. (Rockwell Automation)

5. Maintenance and uptime (predictive + autonomous) 

What’s getting automated: AI-driven predictive maintenance, automated alerts, condition monitoring, spare-parts planning.

Why in 2026:

• Downtime is the universal enemy. Predictive maintenance keeps legacy equipment viable while plants modernize gradually.
  
  

AI maturity is rising fast. Manufacturers are broadly investing in AI/ML and increasingly using it to anticipate failures and schedule maintenance intelligently. (NIST)

6. Planning, scheduling, and supply-chain decisions 

What’s getting automated: AI-assisted production scheduling, automated quoting, dynamic inventory decisions.

Why in 2026:

• Uncertainty demands adaptability. Tariffs, demand swings, supplier volatility — plants need schedules that can re-optimize quickly. (Reuters)
  
  
• AI is moving upstream. IDC predicts a large share of manufacturers will upgrade scheduling with AI to enable more autonomous operations. (IDC Blog)

The common thread: "factory-ready, not science-fair"

Across all these areas, one theme stands out:

Manufacturers are automating what can be deployed quickly, owned by the team, and adapted to real floor conditions.

That’s why we see:

• modular cells over bespoke megaprojects,
  
  
• incremental automation over “rip and replace,”
  
  
• and systems that operators can run confidently without a PhD.
  
  

It’s also why workforce training and adoption are becoming the make-or-break factor. Even the best tech stalls without people who can own it. (Deloitte)

What this means for your 2026 roadmap 

If you’re planning your next automation step, take a cue from what’s winning across the industry:

1. Start where the process is stable and the pain is obvious (end-of-line is the classic example).
   
   
2. Prioritize time-to-value over perfect long-range specs.
   
   
3. Design for variability, not the ideal drawing.
   
   
4. Build operator ownership early so the system scales instead of stalling.
   
   

In 2026, automation isn’t about chasing the most futuristic factory.
It’s about building a Lean, flexible, resilient one, one step at a time.

If you want to see whether palletizing automation makes sense for your facility, start with the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

The Humanoids Summit made one thing very clear: progress in humanoid robotics isn’t being limited by ambition, but instead by data, reliability, and deployment reality.

Across talks, demos, and hallway conversations, a consistent theme emerged. The industry is no longer asking if humanoids will work, but how to train them, evaluate them, and deploy them safely at scale.

Here’s what stood out most.

The real bottleneck: Data

Everyone agrees that high-quality data is the foundation of Physical AI. The nuance isn’t about whether to collect a certain type of data; teams want as much as they can get. The difference is in how they allocate resources across the data spectrum, because each layer comes with its own cost, difficulty, and payoff.

Most teams described some version of a “data pyramid”:

1. Real robot deployment 

This is the gold standard. Real robots performing real tasks generate the most transferable data. The problem?
It doesn’t scale.

Deployments are expensive, slow, and constrained by hardware availability. Even the most advanced teams can only collect so much data this way.

2. Teleoperation

Teleop is becoming a key middle ground.  Some innovations seen were using digital teleoperation along with real world teleoperation. 
We spoke with several startups working on this layer:

• Contact CI with haptic gloves
  
  
• Lightwheel, enabling large-scale digital teleoperation
  
  
• Labryinth AI, VR-based approaches translating human motion into robot joint data
  
  

Teleop data is more scalable than full deployment, but still resource-intensive.

3. Human-centered data (video, motion capture) 

This is the most abundant…and the least transferable.
Human video datasets are widely available, but translating them into reliable robot behavior remains challenging.

The emerging consensus?
Most teams are training models first on large-scale human data, then fine-tuning with teleop and real deployment data. It’s a pragmatic approach to a difficult scaling problem.

The open question remains:
Do humanoids need billions of data points—or trillions? And how efficiently can that data be converted into useful behavior? Will new algorithms grounded in physics and kinematics alleviate the data dependency problem?

Two competing philosophies: Generalization vs deployment  

Another major divide at the summit centered on where to focus effort.

The “Generalizable Model” Camp

Companies like Skild AI, Galbot, and others are betting on large, foundational models that can generalize across many tasks. They are playing the long game: building massive datasets, simulation pipelines, and broad reasoning capabilities.

The upside is clear: long-term flexibility.
The risk is just as clear: long timelines, high burn rates, and limited near-term deployment.

The “Reliable Deployment” Camp

Other companies are prioritizing application-ready humanoids:

• Agility
  
• Field AI
  
• Persona
  
• torqueAGI
  
  

These teams are focusing on reliability, safety, and narrow but valuable use cases. Agility stood out by having humanoids working in warehouses for real clients.

Their message was consistent:
If the robot isn’t reliable, a human has to supervise it, and then the ROI disappears.

World models, foundational models, and a missing piece: Evaluation 

Many speakers focused on the emergence of World Foundation Models—systems with broad ability to understand physical interactions. The conversation centered around figuring out the best way to build and train them: what data they need, how they generalize across environments, and how much physical interaction is required to learn meaningful behaviors.

High-fidelity world models are hard to build because they require extremely accurate physical data. Even harder? Evaluating progress.

Right now, there’s no standard way to measure whether a world model is truly improving real-world task performance. NVIDIA’s upcoming evaluation arenas were mentioned as a promising step, but this remains an open challenge.

Where humanoids actually make sense today  

Agility presented one of the clearest frameworks for humanoid value:

Humanoids shine where you need:

• Mobility in cluttered, changing environments
  
  
• Flexibility to rotate between multiple tasks
  
  
• Dynamic stability to pick, lift, and move payloads from awkward positions
  
  

One compelling example was using a humanoid to link two semi-fixed but unstructured systems—like moving goods from a shelf on an AMR to a conveyor. These are workflows that are awkward for traditional robots but natural for human-shaped machines.

Deployment reality: Configuration, reliability, safety 

Several themes came up repeatedly when discussing real-world deployment:

• Configurability: If deployment isn’t straightforward, you lose flexibility—the core humanoid value proposition.
  
  
• Reliability: Unreliable robots simply shift work instead of eliminating it.
  
  
• Safety: At scale, humanoids must be robustly safe.

These challenges mirror what manufacturers already know from collaborative automation: technology only creates value when it works consistently, safely, and predictably.

Hands vs grippers: A surprising consensus 

One of the most animated debates was about hands versus grippers.

Despite impressive demos of anthropomorphic hands, most practitioners were candid:

• Hands are hard to control
  
  
• They are difficult to deploy reliably
  
  
• Dexterity adds significant complexity
  
  

The prevailing view was pragmatic:
Grippers (especially bimanual setups) will dominate in the near term.

They solve the majority of manipulation tasks with far less complexity. Dexterous hands may arrive later, but grasping comes first.

That said, interest in tactile sensing was strong. Researchers and companies are exploring:

• How to structure tactile and haptic data
  
  
• What robots should actually measure
  
  
• How to visualize and use contact information effectively

What this means for Robotiq 

From a Robotiq perspective, a few conclusions stand out:

• The humanoid ecosystem needs feature-dense, scalable, reliable hardware
  
  
• Ease of integration, from hardware to software and communication is essential, which is where Robotiq’s plug-and-play mentality fits nicely
  
  
• Grippers will remain central to real-world Physical AI in the near term
  
  
• Force-torque and tactile sensing are increasingly relevant, from humanoids to prosthetics
  
  
• Customization (fingertips, form factors) will matter for emerging manipulation tasks like scooping or cloth handling
  
  

Perhaps most importantly, the summit reinforced a familiar lesson: automation succeeds when it moves from impressive demos to operational reliability.

Final takeaway

Humanoid robotics is progressing rapidly—but not linearly. The companies making real progress are the ones grappling seriously with data quality, deployment constraints, and safety at scale.

The future of Physical AI won’t be decided by the flashiest demo. It will be decided by who can deliver reliable systems, trained on the right data, solving real problems—day after day.

That’s where humanoids stop being research projects and start becoming tools.

At the event in Las Vegas, Robotiq partnered with Universal Robots and Siemens to demonstrate a next-generation palletizing solution that combines collaborative robotics, lean palletizing, and real-time digital twin technology.

Lean Palletizing you can simulate, see, and run

At the Siemens booth, attendees experience a live demonstration that brought together:

• Robotiq’s PAL Ready palletizing cell
• Universal Robots’ UR20 collaborative robot
• Siemens automation hardware and Digital Twin Composer software
  
  

The palletizing cell is rendered photo-realistically in real time using a digital twin, while the physical system operats live on the show floor. This digital-meets-physical setup allows visitors to understand how the palletizer behaves before, during, and after operation.

For manufacturers, this approach reduces uncertainty and shortens the path from planning to deployment.

Why digital twin palletizing matters for manufacturers

Palletizing is a critical end-of-line process, but it is often constrained by space, product variability, and workforce availability. Digital twin technology makes it possible to address these constraints earlier and with more confidence.
By combining lean palletizing with real-time simulation and analytics, manufacturers can:

• Validate palletizing layouts before installation
• Optimize robot motion and gripping strategies
• Predict performance and cycle times
• Reduce commissioning risk and downtime

This is where digital AI meets physical AI, creating automation systems that are easier to deploy and easier to scale.

Lean Palletizing enhanced with real-time data

During the demonstration, the system palletized boxes of chips and beverages while digital twin analytics dynamically optimized gripper performance and suction points. Data was captured using Siemens Industrial Edge hardware and streamed to Siemens Insights Hub Copilot, providing real-time visibility into palletizer behavior.

This closed loop between simulation and reality enables faster adjustments and more predictable palletizing performance without redesigning the cell.

As our CEO, Samuel Bouchard, explains:

“This collaboration with Universal Robots and Siemens demonstrates how lean palletizing, combined with cutting-edge digital twin technology, can help companies boost efficiency and adapt quickly to changing demands.”

Designed for real-world ROI

A key takeaway from CES 2026 is not just the technology itself, but how accessible it has become. The integration of the UR20, PAL Ready, and Siemens’ digital environment shows how manufacturers can deploy advanced palletizing automation without excessive engineering or long lead times.

This approach supports:

• Faster return on investment
• Lower deployment risk
• Easier adaptation to new products and layouts

Jean-Pierre Hathout, President of the Teradyne Robotics Group, sums it up clearly:

“The seamless integration of our UR20 robot with Robotiq’s palletizing cell and Siemens’ advanced automation and software shows how digital and physical innovation can work hand-in-hand to transform production environments and deliver measurable ROI.”

What this signals for the future of palletizing automation

This collaboration highlights a broader shift in industrial automation. Manufacturers are looking for systems that are easier to plan, easier to operate, and flexible enough to evolve with their business.

The future of palletizing automation includes:

• Digital twins that support deployment decisions
• Lean robotic cells that adapt to variability
• Open platforms that avoid lock-in
• Colaborative robots designed for fast, scalable ROI

At Robotiq, our goal is to make palletizing automation practical, predictable, and accessible. CES 2026 shows that when lean robotics and digital twins come together, manufacturers no longer have to choose between flexibility and performance.
The technology is ready. The applications are real. And the factory floor is where it delivers value.

If you want to see whether palletizing automation makes sense for your facility, start with the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

When manual palletizing holds the line back

RAIN faced familiar challenges for mid-sized beverage manufacturers:

• Labor-intensive tasks: Two operators stacked heavy corrugated cases all shift long.
• SKU complexity: More product variations increased the risk of stacking and labeling mistakes.
• Limited space: Traditional palletizing equipment was too large for their facility.

Even with engineering expertise in-house, building a custom palletizer would have meant long lead times and higher risk.

A compact, ready-to-run fix

RAIN selected the Robotiq PE20 Lean Palletizer, powered by a collaborative robot from Universal Robots. The system delivered the speed and reliability they needed—without major layout changes.

Key advantages for RAIN:

• Small footprint that fit their existing line
• Integrated safety scanner that reduced guarding requirements
• Fast deployment with minimal downtime
• Easy programming, even without robotics experience

Founder Mark Majkrzak summed it up simply:

“This solution improves throughput, operator health and safety, gross production and labor utilization. All without anyone losing their job.”

Turning heavy lifting into high-value work

Automation didn’t replace RAIN’s team; it elevated them.
The two operators previously dedicated to palletizing now hold forklift certifications and support higher-value areas of the plant.

Meanwhile, the PE20 runs consistently and keeps pace with production. RAIN got:

• Immediate throughput gains
  
• A safer, more ergonomic workflow
  
• A faster-than-expected ROI

What this means for beverage manufacturers

RAIN’s experience reflects a pattern across the Food & Beverage industry. Compact cobot palletizers remove the most physically demanding end-of-line task. They scale with SKU changes. They fit where traditional systems can’t. And they deliver results in weeks, not months.

If you want to see whether palletizing automation makes sense for your facility, start here. 

 Try the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

Want more stories from real factories like yours?

Follow Robotiq on LinkedIn and join over 75,000 manufacturers seeing how automation keeps people safe, and production running strong.

That second part didn’t happen by accident.

It’s the result of a bold decision Cascade made about four years ago: replace one of the most painful, turnover-heavy, inconsistent manual jobs in the factory with collaborative robots. What started as a single low-risk experiment snowballed into a systematic, company-wide transformation touching productivity, people, and culture.

I recently sat down with Cascade’s COO, Ron Kane, to retrace that journey. Ron’s spent more than 30 years in food and beverage—Nestlé Waters, Monster Energy, craft beer—so he’s seen every flavor of automation project, from the successful to the painful. His perspective is extremely grounded in what manufacturers live every day.

Here’s Cascade’s story, and the lessons any food and bev factory can apply.

When growth meets reality

In 2020, Cascade shifted from serving one major customer (80% of their volume) to supporting a broad portfolio of brands, especially innovative ecommerce players. It unlocked growth, but increased complexity overnight.

The problem? Nearly everything downstream was still manual.

• Operators were hand-stacking dozens of pallet configurations
• Turnover at the palletizing role was over 60%
  
• New hires were trained almost weekly on intricate customer-specific patterns
• Quality errors—like misaligned labels on shipments—were eroding customer satisfaction

And culturally, nobody wanted the palletizing job. It was physically demanding, low variety, and stressful.

“It didn’t provide value for us as a factory. It needed to be done, but it really should have been automated if there was a way to do it.”
- Ron Kane, COO

The challenge was the same one many mid-sized manufacturers face: traditional palletizers were too big, too expensive, and too complex for their footprint.

The first leap: A practical, low-risk test

When Cascade purchased a new high-volume K-Cup line, Ron saw an opportunity: if they were upgrading processing and packaging, why stop there?

He evaluated the full picture: labor, ergonomics, turnover, uptime, and customer requirements and proposed trialing a cobot palletizer. The initial ROI estimate was just under two years, already solid.

What happened next surprised everyone.

Their local partner, Olympus Controls, rolled the system into place. Within hours, it was bolted down. By the next afternoon, it was palletizing live production.

No cages. No complex programming. No weeks of commissioning.

Operators learned the interface in minutes. The safety behavior immediately earned their trust. And the cultural shift happened faster than expected: the team named the robot, decorated it for holidays, and treated it as an additional coworker.

“We installed the robot, didn’t lose any headcount, and it became part of the personality of the factory.”

Nine months to payback, not two years

When the first audit was complete, the numbers came in...

Actual ROI: 9 months.

Why the dramatic difference?

• Higher uptime than expected
• Consistent throughput, even during variable volume
• No unplanned labor coverage for downtime
• Fewer quality issues
  
• Rising volume on the line

In three years, Ron can count on one hand the number of days any palletizer has been down. And those rare issues? Usually from packaging anomalies, not the robot.

That reliability became the reason Cascade could scale confidently.

Scaling systematically: From one to six (and counting!)

Once the first cell proved itself, Cascade replicated the approach across every retail bag line in the factory.

Because their mechanics and engineers already understood the interface, subsequent installations became nearly plug-and-play:

• Still delivered in crates
• Still bolted down in hours
• Still operational the next day
• Still easy to troubleshoot independently

One line went from crate arrival to running production by the end of the first shift.

And their team started collaborating with our engineers on performance upgrades—like early adoption of dual-case picking to boost throughput without increasing speed.

The result was: a standardized, repeatable palletizing strategy across the entire site.

Real business impact: Millions saved, people lifted up

Cascade Coffee invested just under seven figures across all systems.

They’ve already recouped multiple millions in labor efficiency.

But the part Ron speaks about with the most pride isn’t the financial return; it’s the people.

The employees who once rotated through the hardest job in the plant are still there. Many have moved up to higher-skill roles.

“They’re building careers, not just doing jobs. They’re earning more for their families. They’re more confident operating bigger equipment because they learned on the cobot first.”

Automation didn’t reduce headcount. It actually created new opportunity.

And it was a great fit culturally as well. The robots have names, holiday outfits, and recurring appearances on Cascade’s LinkedIn page. They’ve become part of the team!

Lessons for manufacturers considering automation

Here are the takeaways Ron would give anyone just starting their automation journey:

1. Your real ROI will likely be better than your spreadsheet.

Manufacturers often underestimate the effect of consistent throughput and overestimate downtime risk.

2. Start where the pain is highest and the labor is toughest to retain.

Palletizing jobs burn people out quickly. Solving that pain benefits the whole operation.

3. Culture matters as much as technology.

Communicate clearly that automation removes undesirable tasks—not people.

4. Standardize early.

Once one cell is running well, you can replicate it efficiently across similar lines.

5. A strong local partner makes all the difference.

Olympus Controls helped Cascade deploy fast and adapt quickly, even as needs changed.

What this story really shows

Automation doesn’t have to be expensive, risky, or disruptive. When done systematically (and with people in mind) it becomes a catalyst for better work, better throughput, and better business.

Cascade Coffee went from manual palletizing with high turnover to a fully automated, highly engaged team that’s confident, growing, and ready to take on more.

Their journey is exactly why we believe in Lean Robotics: start small, scale fast, and build capability through every phase: design, integrate, operate.

Ron said it best:

“You gave us the first steps. We wouldn’t be pursuing automation this aggressively without that early success.”

And that’s the kind of impact we want every manufacturer to experience.

If you want to see whether palletizing automation makes sense for your facility, start with the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

Want more stories from real factories like yours?

Follow Robotiq on LinkedIn and join over 75,000 manufacturers seeing how automation keeps people safe, and production running strong.

We’re also grateful for the power of smart automation: technology that doesn’t just boost output, but protects people, improves quality, and makes factories safer and more sustainable.

To celebrate, we’re sharing five real-world stories where Robotiq’s collaborative palletizing solutions have made a meaningful impact. These examples show what’s possible when automation and human talent work hand in hand.

1. Glenhaven Foods: Fast ROI, safer operators

The Challenge: Glenhaven Foods faced high labor costs and ergonomic strain from manual palletizing of frozen meals. They needed a solution that could handle dozens of SKUs without compromising worker safety.

The Solution: By implementing Robotiq’s PE20 Lean Palletizer, Glenhaven Foods automated 15 boxes per minute across 25 SKUs. The system was up and running quickly, with minimal training required.

The Result: Operators were freed from repetitive, high-strain tasks, productivity increased, and the ROI was achieved in just 13 months. Lean Palletizing became an integral part of their operations, supporting both output and workplace safety.

2. RAIN Pure Mountain Spring Water: Reducing strain, boosting throughput

The Challenge: RAIN Pure Mountain Spring Water’s operators were lifting heavy 30-pound boxes in a compact facility, a physically demanding task that contributed to fatigue and turnover.

The Solution: Robotiq’s compact palletizing solution enabled safe, collaborative automation right alongside human operators.

The Result: Through automation, operators were reassigned to higher-value tasks, throughput increased, and ergonomic risks were significantly reduced. This allowed the team to focus on what matters most: consistent quality and efficiency.

3. Griffith Foods Colombia: Eliminating thousands of repetitive motions

The Challenge: Repetitive manual stacking was creating fatigue, ergonomic risks, and slowdowns on multiple production lines with many SKUs.

The Solution: Lean Palletizing, powered by Robotiq cobots, replaced thousands of daily repetitive motions with automated precision.

The Result: Operators now enjoy safer, less physically demanding work, while the production line runs more consistently. Safety and productivity went hand in hand, proving that even repetitive, “back-of-line” tasks can benefit from automation.

4. Panovo (Grupo Proan): Handling tall stacks with ease

The Challenge: Bakery production lines faced high pallet stacks (up to 2.5 meters/ 8+ feet), causing fatigue and inconsistent pallet quality.

The Solution: Five AX10 cobots were deployed to handle palletizing, with carefully engineered configurations to manage tall stacks safely.

The Result: The cobots consistently stack tall pallets without fatigue or error, improving workflow, operator well-being, and pallet stability. Panovo now runs a smoother operation while keeping employees out of risky manual tasks.

5. Korea Pelagic: Automation in a cold, demanding environment 

The Challenge: Seafood processing requires handling heavy, cold items with limited workforce availability and high turnover risk. Manual palletizing was slow, unsafe, and inconsistent.

The Solution: Robotiq cobots automated palletizing in the cold environment, working safely alongside humans.

The Result: Productivity increased by 200%, labor strain decreased, and turnover stabilized. Operators can focus on higher-value tasks, while cobots handle repetitive, physically demanding work.

Why these stories matter

Across these five case studies, a few clear patterns emerge:

• Automation protects people: From heavy lifting to repetitive motions, cobots handle high-risk tasks safely.
• Automation supports business growth: Increased throughput, predictable ROI, and faster commissioning make these investments worthwhile.
• Automation complements human talent: Operators are freed for higher-value work, leading to safer, more engaging jobs and lower turnover.

At Robotiq, we regularly share these real-world customer stories to show what’s possible when humans and robots work together.

A Thanksgiving Message

This holiday season, we’re thankful for all the Food & Beverage teams who make innovation, safety, and efficiency a reality.

From Glenhaven Foods’ fast ROI to Korea Pelagic’s cold-environment success, these stories remind us why automation is about more than machines; it’s about supporting the people who make factories thrive.

Here’s to full pallets, safer workflows, and smarter operations this Thanksgiving.

Discover your own path to safer, smarter palletizing. Try the Palletizing Fit Tool — a quick, interactive way to see if Lean Palletizing is the right match for your line.

Want more stories from real factories like yours?

Follow Robotiq on LinkedIn and join over 75,000 manufacturers seeing how automation keeps people safe, and production running strong.

By Michael Allen

In the critical 72 hours after an earthquake or explosion, a race against the clock begins to find survivors. After that window, the chances of survival drop sharply.

When a powerful earthquake hit central Italy on 24 August 2016, killing 299 people, over 5 000 emergency workers were mobilised in search and rescue efforts that saved dozens from the rubble in the immediate aftermath.

The pressure to move fast can create risks for first responders, who often face unstable environments with little information about the dangers ahead. But this type of rescue work could soon become safer and more efficient thanks to a joint effort by EU and Japanese researchers.

Supporting first responders

Rescue organisations, research institutes and companies from both Europe and Japan worked together from 2019 to 2023 to develop a new generation of tools blending robotics, drone technology and chemical sensing to transform how emergency teams operate in disaster zones.

It is a prototype technology that did not exist before.
– Tiina Ristmäe, CURSOR

Their work was part of a four-year EU-funded international research initiative called CURSOR, which included partners from six EU countries, Norway and the UK. It also included Tohoku University, whose involvement was funded by the Japan Science and Technology Agency.

The researchers hope that the sophisticated rescue kit they have developed will help rescue workers locate trapped survivors faster, while also improving their own safety.

“In the field of search and rescue, we don’t have many technologies that support first responders, and the technologies that we do have, have a lot of limitations,” said Tiina Ristmäe, a research coordinator at the German Federal Agency for Technical Relief and vice president of the International Forum to Advance First Responder Innovation.

Meet the rescue bots

At the heart of the researcher’s work is a small robot called Soft Miniaturised Underground Robotic Finder (SMURF). The robot is designed to navigate through collapsed buildings and rubble piles to locate people who may be trapped underneath.

The idea is to allow rescue teams to do more of their work remotely, localising and finding humans from the most hazardous areas in the early stages of a rescue operation. The SMURF can be remotely controlled by operators who stay at a safe distance from the rubble.

“It is a prototype technology that did not exist before,” said Ristmäe. “We don’t send people, we send machines – robots – to do the often very dangerous job.”

The SMURF is compact and lightweight, with a two-wheel design that allows it to manoeuvre over debris and climb small obstacles.

“It moves and drops deep into the debris to find victims, with multiple robots covering the whole rubble pile,” said Professor Satoshi Tadokoro, a robotics expert at Tohoku University and one of the project’s lead scientists.

The development team tested many designs before settling on the final SMURF prototype.

“We investigated multiple options – multiple wheels or tracks, flying robots, jumping robots – but we concluded that this two-wheeled design is the most effective,” said Tadokoro.

Sniffing for survivors

The SMURF’s small “head” is packed with technology: video and thermal cameras, microphones and speakers for two-way communication, and a powerful chemical sensor known as the SNIFFER.

This sensor is capable of detecting substances that humans naturally emit, such as C02 and ammonia, and can even distinguish between living and deceased individuals.

Put to the test in real-world conditions, the SNIFFER has proved able to provide reliable information even when surrounded by competing stimuli, like smoke or rain.

According to the first responders who worked with the researchers, the information provided by the SNIFFER is highly valuable: it helps them to prioritise getting help to those who are still alive, said Ristmäe.

Drone delivery

To further improve the reach of the SMURF, the researchers also integrated drone support into the system. Customised drones are used to deliver the robots directly to the areas where they’re needed most – places that may be hard or dangerous to access on foot.

Ιt moves and drops deep into the debris to find victims, with multiple robots covering the whole rubble pile.
– Professor Satoshi Tadokoro, Tohoku University

“You can transport several robots at the same time and drop them in different locations,” said Ristmäe.

Alongside these delivery drones, the CURSOR team developed a fleet of aerial tools designed to survey and assess disaster zones. One of the drones, dubbed the “mothership,” acts as a flying communications hub, linking all the devices on the ground with the rescue team’s command centre.

Other drones carry ground-penetrating radar to detect victims buried beneath debris. Additional drones capture overlapping high-definition footage that can be stitched together into detailed 3D maps of the affected area, helping teams to visualise the layout and plan their operations more strategically.

Along with speeding up search operations, these steps should slash the time emergency workers spend in dangerous locations like collapsed buildings.

Testing in the field

The combined system has already undergone real-world testing, including large-scale field trials in Japan and across Europe.

One of the most comprehensive tests took place in November 2022 in Afidnes, Greece, where the full range of CURSOR technologies was used in a simulated disaster scenario.

Though not yet commercially available, the prototype rescue kit has sparked global interest.

“We’ve received hundreds of requests from people wanting to buy it,” said Ristmäe. “We have to explain it’s not deployable yet, but the demand is there.”

The CURSOR team hopes to secure more funding to further enhance the technology and eventually bring it to market, potentially transforming the future of disaster response.

Research in this article was funded by the EU’s Horizon Programme. The views of the interviewees don’t necessarily reflect those of the European Commission. If you liked this article, please consider sharing it on social media.

This article was originally published in Horizon, the EU Research and Innovation magazine.

By Melissa Anchisi and Florian Meyer

In July, EPFL, ETH Zurich, and the Swiss National Supercomputing Centre (CSCS) announced their joint initiative to build a large language model (LLM). Now, this model is available and serves as a building block for developers and organisations for future applications such as chatbots, translation systems, or educational tools.

The model is named Apertus – Latin for “open” – highlighting its distinctive feature: the entire development process, including its architecture, model weights, and training data and recipes, is openly accessible and fully documented.

AI researchers, professionals, and experienced enthusiasts can either access the model through the strategic partner Swisscom or download it from Hugging Face – a platform for AI models and applications – and deploy it for their own projects. Apertus is freely available in two sizes – featuring 8 billion and 70 billion parameters, the smaller model being more appropriate for individual usage. Both models are released under a permissive open-source license, allowing use in education and research as well as broad societal and commercial applications.

A fully open-source LLM

As a fully open language model, Apertus allows researchers, professionals and enthusiasts to build upon the model and adapt it to their specific needs, as well as to inspect any part of the training process. This distinguishes Apertus from models that make only selected components accessible.

“With this release, we aim to provide a blueprint for how a trustworthy, sovereign, and inclusive AI model can be developed,” says Martin Jaggi, Professor of Machine Learning at EPFL and member of the Steering Committee of the Swiss AI Initiative. The model will be regularly updated by the development team which includes specialized engineers and a large number of researchers from CSCS, ETH Zurich and EPFL.

A driver of innovation

With its open approach, EPFL, ETH Zurich and CSCS are venturing into new territory. “Apertus is not a conventional case of technology transfer from research to product. Instead, we see it as a driver of innovation and a means of strengthening AI expertise across research, society and industry,” says Thomas Schulthess, Director of CSCS and Professor at ETH Zurich. In line with their tradition, EPFL, ETH Zurich and CSCS are providing both foundational technology and infrastructure to foster innovation across the economy.

Trained on 15 trillion tokens across more than 1,000 languages – 40% of the data is non-English – Apertus includes many languages that have so far been underrepresented in LLMs, such as Swiss German, Romansh, and many others.

“Apertus is built for the public good. It stands among the few fully open LLMs at this scale and is the first of its kind to embody multilingualism, transparency, and compliance as foundational design principles”, says Imanol Schlag, technical lead of the LLM project and Research Scientist at ETH Zurich.

“Swisscom is proud to be among the first to deploy this pioneering large language model on our sovereign Swiss AI Platform. As a strategic partner of the Swiss AI Initiative, we are supporting the access of Apertus during the Swiss {ai} Weeks. This underscores our commitment to shaping a secure and responsible AI ecosystem that serves the public interest and strengthens Switzerland’s digital sovereignty”, commented Daniel Dobos, Research Director at Swisscom.

Accessibility

While setting up Apertus is straightforward for professionals and proficient users, additional components such as servers, cloud infrastructure or specific user interfaces are required for practical use. The upcoming Swiss {ai} Weeks hackathons will be the first opportunity for developers to experiment hands-on with Apertus, test its capabilities, and provide feedback for improvements to future versions.

Swisscom will provide a dedicated interface to hackathon participants, making it easier to interact with the model. As of today, Swisscom business customers will be able to access the Apertus model via Swisscom’s sovereign Swiss AI platform.

Furthermore, for people outside of Switzerland, the Public AI Inference Utility will make Apertus accessible as part of a global movement for public AI. “Currently, Apertus is the leading public AI model: a model built by public institutions, for the public interest. It is our best proof yet that AI can be a form of public infrastructure like highways, water, or electricity,” says Joshua Tan, Lead Maintainer of the Public AI Inference Utility.

Transparency and compliance

Apertus is designed with transparency at its core, thereby ensuring full reproducibility of the training process. Alongside the models, the research team has published a range of resources: comprehensive documentation and source code of the training process and datasets used, model weights including intermediate checkpoints – all released under the permissive open-source license, which also allows for commercial use. The terms and conditions are available via Hugging Face.

Apertus was developed with due consideration to Swiss data protection laws, Swiss copyright laws, and the transparency obligations under the EU AI Act. Particular attention has been paid to data integrity and ethical standards: the training corpus builds only on data which is publicly available. It is filtered to respect machine-readable opt-out requests from websites, even retroactively, and to remove personal data, and other undesired content before training begins.

The beginning of a journey

“Apertus demonstrates that generative AI can be both powerful and open,” says Antoine Bosselut, Professor and Head of the Natural Language Processing Laboratory at EPFL and Co-Lead of the Swiss AI Initiative. “The release of Apertus is not a final step, rather it’s the beginning of a journey, a long-term commitment to open, trustworthy, and sovereign AI foundations, for the public good worldwide. We are excited to see developers engage with the model at the Swiss {ai} Weeks hackathons. Their creativity and feedback will help us to improve future generations of the model.”

Future versions aim to expand the model family, improve efficiency, and explore domain-specific adaptations in fields like law, climate, health and education. They are also expected to integrate additional capabilities, while maintaining strong standards for transparency.

At this year’s International Conference on Machine Learning (ICML2025), Jaeho Kim, Yunseok Lee and Seulki Lee won an outstanding position paper award for their work Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards. We hear from Jaeho about the problems they were trying to address, and their proposed author feedback mechanism and reviewer reward system.

Could you say something about the problem that you address in your position paper?

Our position paper addresses the problems plaguing current AI conference peer review systems, while also raising questions about the future direction of peer review.

The imminent problem with the current peer review system in AI conferences is the exponential growth in paper submissions driven by increasing interest in AI. To put this with numbers, NeurIPS received over 30,000 submissions this year, while ICLR saw a 59.8% increase in submissions in just one year. This huge increase in submissions has created a fundamental mismatch: while paper submissions grow exponentially, the pool of qualified reviewers has not kept pace.

Submissions to some of the major AI conferences over the past few years.

This imbalance has severe consequences. The majority of papers are no longer receiving adequate review quality, undermining peer review’s essential function as a gatekeeper of scientific knowledge. When the review process fails, inappropriate papers and flawed research can slip through, potentially polluting the scientific record.

Considering AI’s profound societal impact, this breakdown in quality control poses risks that extend far beyond academia. Poor research that enters the scientific discourse can mislead future work, influence policy decisions, and ultimately hinder genuine knowledge advancement. Our position paper focuses on this critical question and proposes methods on how we can enhance the quality of review, thus leading to better dissemination of knowledge.

What do you argue for in the position paper?

Our position paper proposes two major changes to tackle the current peer review crisis: an author feedback mechanism and a reviewer reward system.

First, the author feedback system enables authors to formally evaluate the quality of reviews they receive. This system allows authors to assess reviewers’ comprehension of their work, identify potential signs of LLM-generated content, and establish basic safeguards against unfair, biased, or superficial reviews. Importantly, this isn’t about penalizing reviewers, but rather creating minimal accountability to protect authors from the small minority of reviewers who may not meet professional standards.

Second, our reviewer incentive system provides both immediate and long-term professional value for quality reviewing. For short-term motivation, author evaluation scores determine eligibility for digital badges (such as “Top 10% Reviewer” recognition) that can be displayed on academic profiles like OpenReview and Google Scholar. For long-term career impact, we propose novel metrics like a “reviewer impact score” – essentially an h-index calculated from the subsequent citations of papers a reviewer has evaluated. This treats reviewers as contributors to the papers they help improve and validates their role in advancing scientific knowledge.

Could you tell us more about your proposal for this new two-way peer review method?

Our proposed two-way peer review system makes one key change to the current process: we split review release into two phases.

The authors’ proposed modification to the peer-review system.

Currently, authors submit papers, reviewers write complete reviews, and all reviews are released at once. In our system, authors first receive only the neutral sections – the summary, strengths, and questions about their paper. Authors then provide feedback on whether reviewers properly understood their work. Only after this feedback do we release the second part containing weaknesses and ratings.

This approach offers three main benefits. First, it’s practical – we don’t need to change existing timelines or review templates. The second phase can be released immediately after the authors give feedback. Second, it protects authors from irresponsible reviews since reviewers know their work will be evaluated. Third, since reviewers typically review multiple papers, we can track their feedback scores to help area chairs identify (ir)responsible reviewers.

The key insight is that authors know their own work best and can quickly spot when a reviewer hasn’t properly engaged with their paper.

Could you talk about the concrete reward system that you suggest in the paper?

We propose both short-term and long-term rewards to address reviewer motivation, which naturally declines over time despite starting enthusiastically.

Short-term: Digital badges displayed on reviewers’ academic profiles, awarded based on author feedback scores. The goal is making reviewer contributions more visible. While some conferences list top reviewers on their websites, these lists are hard to find. Our badges would be prominently displayed on profiles and could even be printed on conference name tags.
Example of a badge that could appear on profiles.

Long-term: Numerical metrics to quantify reviewer impact at AI conferences. We suggest tracking measures like an h-index for reviewed papers. These metrics could be included in academic portfolios, similar to how we currently track publication impact.

The core idea is creating tangible career benefits for reviewers while establishing peer review as a professional academic service that rewards both authors and reviewers.

What do you think could be some of the pros and cons of implementing this system?

The benefits of our system are threefold. First, it is a very practical solution. Our approach doesn’t change current review schedules or review burdens, making it easy to incorporate into existing systems. Second, it encourages reviewers to act more responsibly, knowing their work will be evaluated. We emphasize that most reviewers already act professionally – however, even a small number of irresponsible reviewers can seriously damage the peer review system. Third, with sufficient scale, author feedback scores will make conferences more sustainable. Area chairs will have better information about reviewer quality, enabling them to make more informed decisions about paper acceptance.

However, there is strong potential for gaming by reviewers. Reviewers might optimize for rewards by giving overly positive reviews. Measures to counteract these problems are definitely needed. We are currently exploring solutions to address this issue.

Are there any concluding thoughts you’d like to add about the potential future
of conferences and peer-review?

One emerging trend we’ve observed is the increasing discussion of LLMs in peer review. While we believe current LLMs have several weaknesses (e.g., prompt injection, shallow reviews), we also think they will eventually surpass humans. When that happens, we will face a fundamental dilemma: if LLMs provide better reviews, why should humans be reviewing? Just as the rapid rise of LLMs caught us unprepared and created chaos, we cannot afford a repeat. We should start preparing for this question as soon as possible.

About Jaeho

Jaeho Kim is a Postdoctoral Researcher at Korea University with Professor Changhee Lee. He received his Ph.D. from UNIST under the supervision of Professor Seulki Lee. His main research focuses on time series learning, particularly developing foundation models that generate synthetic and human-guided time series data to reduce computational and data costs. He also contributes to improving the peer review process at major AI conferences, with his work recognized by the ICML 2025 Outstanding Position Paper Award.

Read the work in full

Position: The AI Conference Peer Review Crisis Demands Author Feedback and Reviewer Rewards, Jaeho Kim, Yunseok Lee, Seulki Lee.

An important aspect of autonomous soccer-playing robots concerns accurate detection of the ball. This is the focus of work by Can Lin, Daniele Affinita, Marco Zimmatore, Daniele Nardi, Domenico Bloisi, and Vincenzo Suriani, which won the best paper award at the recent RoboCup symposium. The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition.

Could you start by giving us a brief description of the problem that you were trying to solve in your paper “Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots”?

Daniele Affinita: The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online. But when the task is highly specific, like in RoboCup, you need to collect and label the data yourself. That means gathering the data and manually annotating it before you can even start applying deep learning. This process is not scalable and demands a significant human effort.

The idea behind our paper was to reduce this human effort. We approached the problem through self-supervised learning, which aims to learn useful representations of the data. After all, deep learning is essentially about learning latent representations from the available data.

Could you tell us a bit more about your self-supervised learning framework and how you went about developing it?

Daniele: First of all, let me introduce what self-supervised learning is. It is a way of learning the structure of the data without having access to labels. This is usually done through what we call pretext tasks. These are tasks that don’t require explicit labels, but instead exploit the structure of the data. For example, in our case we worked with images. You can randomly mask some patches and train the model to predict the missing parts. By doing so, the model is forced to learn meaningful features from the data.

In our paper, we enriched the data by using not only raw images but also external guidance. This came from a larger model which we refer to as the teacher. This model was trained on a different task which is more general than the target task we aimed for. This way the larger model can provide guidance (an external signal) that helps the self-supervision to focus more on the specific task we care about.

In our case, we wanted to predict a tight circle around the ball. To guide this, we used an external pretrained model (YOLO) for object detection, which instead predicts a loose bounding box around the ball. We can arguably say that the bounding box, a rectangle, is more general than a circle. So in this sense, we were trying to use external guidance that doesn’t solve exactly the underlying task.

Overview of the data preparation pipeline.

Were you able to test this model out at RoboCup 2025?

Daniele: Yes, we deployed it at RoboCup 2025 and showed great improvements over our previous benchmark, which was the model we used in 2024. In particular, we noticed that the final training requires much less data. The model was also more robust under different lighting conditions. The issue we had with previous models was that they were tailored for specific situations. But of course, all the venues are different, the lighting and the brightness are different, there might be shadows on the field. So it’s really important to have a reliable model and we really noticed a great improvement this year.

What’s your team name, and could you talk a bit about the competition and how it went?

Daniele: So our team is SPQR. We are from Rome, and we have been competing in RoboCup for a long time.

Domenico Blois: We started in 1998, so we are one of the oldest teams in RoboCup.

Daniele: Yeah, I wasn’t even born then! Our team started with the four-legged robots. And then the league shifted more towards biped robots because they are more challenging, they require balance and, overall it’s harder to walk on just two legs.

Our team has grown a lot during recent years. We have been following a very positive trend, going from 9th place in 2019 to third place at the German Open in 2025, and we got 4th place at RoboCup 2025. Our recent success has attracted more students to the team. So it’s kind of a loop – you win more, you attract more students, and you can work more on the challenges proposed by RoboCup.

SPQR team.

Domenico: I want to add that also, from a research point of view, we have won three best paper awards in the last five years, and we have been proposing some new trends towards, for example, the use of LLMs for coding (as a robot’s behaviour generator under the supervision of a human coach). So we are trying to keep the open research field active in our team. We want to win the matches but we also want to solve the research problems that are bound together with the competition.

One of the important contributions of our paper is towards the use of our algorithms outside RoboCup. For example, we are trying to apply the ball detector in precision farming. We want to use the same approach to detect rounded fruits. This is something that is really important for us; to exit the context of Robocup and to use Robocup tools for new approaches in other fields. So if we lose a match, it’s not a big deal for us. We want our students, our team members, to be open minded towards the use of RoboCup as a starting point for understanding teamwork and for understanding how to deal with strict deadlines. This is something that RoboCup can give us. We try to have a team that is ready for every type of challenge, not only within RoboCup, but also other types of AI applications. Winning is not everything for us. We’d prefer to use our own code and not win, than win using code developed by others. This is not optimal for achieving first place, but we want to teach our students to be prepared for the research that is outside of RoboCup.

You said that you’ve previously won two other best paper awards. What did those papers cover?

Domenico: So the last two best papers were kind of visionary papers. In one paper, we wanted to give an insight in how to use the spectators to help the robots score. For example, if you cheer louder, the robots tend to kick the ball. So this is something that is not actually used in the competition now, but is something more towards the 2050 challenge. So we want to imagine how it will be 10 years from now.

The other paper was called “play everywhere”, so you can, for example, play with different types of ball, you can play outside, you can even play without a specific goal, you can play using Coca-Cola cans as goalposts. So the robot has to have a general approach that is not related to the specific field used in RoboCup. This is in contrast to other teams that are very specific. We have a different approach and this is something that makes it harder for us to win the competition. However, we don’t want to win the competition, we want to achieve this goal of having, in 2050, this match between the RoboCup winners and the FIFA World Cup winners.

I’m interested in what you said about transferring the method for ball detection to farming and other applications. Could you say more about that research?

Vincenzo Suriani: Our lab has been involved in some different projects relating to farming applications. The Flourish project ran from 2015 – 2018. More recently, the CANOPIES project has focussed on precision agriculture for permanent crops where farmworkers can efficiently work together with teams of robots to perform agronomic interventions, like harvesting or pruning.

We have another project that is about detecting and harvesting grapes. There is a huge effort in bringing knowledge back from RoboCup to other projects, and vice versa.

Domenico: Our vision now is to focus on the new generation of humanoid robots. We participated in a new event, the World Humanoid Robot Games, held in Beijing in August 2025, because we want to use the platform of RoboCup for other kinds of applications. The idea is to have a single platform with software that is derived from RoboCup code that can be used for other applications. If you have a humanoid robot that needs to move, you can reuse the same code from RoboCup because you can use the same stabilization, the same vision core, the same framework (more or less), and you can just change some modules and you can have a completely different type of application with the same robot with more or less the same code. We want to go towards this idea of reusing code and having RoboCup as a test bed. It is a very tough test bed, but you can use the results in other fields and in other applications.

Looking specifically at RoboCup, what are your future plans for the team? There are some big changes planned for the RoboCup Leagues, so could you also say how this might affect your plans?

Domenico: We have a very strong team and some of the team members will do a PhD in the coming years. One of our targets was to keep the students inside the university and the research ward, and we were successful in this, because now they are very passionate about the RoboCup competition and about AI in general.

In terms of the changes, there will be a new league within RoboCup that is a merger of the standard platform league (SPL) and the humanoid kid-size league. The humanoid adult-size league will remain, so we need to decide whether to join the new merged league, or move to adult-sized robots. At the moment we don’t have too many details, but what we know is that we will go towards a new era of robots. We acquired robots from Booster and we are now acquiring another G1 robot from Unitree. So we are trying to have a complete family of new robots. And then I think we will go towards the league that is chosen by the other teams in the SPL league. But for now we are trying to organize an event in October in Rome with two other teams to exchange ideas and to understand where we want to go. There will also be a workshop to discuss the research side.

Vincenzo: We are also in discussion about the best size of robot for the competition. We are going to have two different positions, because robots are becoming cheaper and there are teams that are pushing to move more quickly to a bigger platform. On the other hand, there are teams that want to stick with a smaller platform in order to do research on multi agents. We have seen a lot of applications for a single robot but not many applications with a set of robots that are cooperating. And this has been historically one of the core parts of research we did in RoboCup, and also outside of RoboCup.

There are plenty of points of view on which robot size to use, because there are several factors, and we don’t know how fast the world will change in two or three years. We are trying to shape the rules and the conditions to play for next year, but, because of how quickly things are changing, we don’t know what the best decision will be. And also the research we are going to do will be affected by the decision we make on this.

There will be some changes to other leagues in the near future too; the small and middle sizes will close in two years probably, and the simulation league also. A lot will happen in the next five years, probably more than during the last 10-15 years. This is a critical year because the decisions are based on what we can see, what we can spot in the future, but we don’t have all the information we need, so it will be challenging.

For example, the SPL has a big, probably the biggest, community among the RoboCup leagues. We have a lot of teams that are grouping by interest and so there are teams that are sticking to working on this specific problem with a specific platform and teams that are trying to move to another platform and another problem. So even inside the same community we are going to have more than one point of view and hopes for the future. At a certain point we will try to figure out what is the best for all of them.

Daniele: I just want to add that in order to achieve the 2050 challenge, in my opinion, it is necessary to have just one league encompassing everything. So up to this point, different leagues have been focusing on different research problems. There were leagues focusing only on strategy, others focusing only on the hardware, our league focusing mainly on the coordination and dynamic handling of the gameplay. But at the end of the day, in order to compete with humans, there must be only one league bringing all these single aspects together. From my point of view, it totally makes sense to keep merging leagues together.

About the authors

	Daniele Affinita is a PhD student in Machine Learning at EPFL, specializing in the intersection of Machine Learning and Robotics. He has over four years of experience competing in RoboCup with the SPQR team. In 2024, he worked at Sony on domain adaptation techniques. He holds a Bachelor’s degree in Computer Engineering and a Master’s degree in Artificial Intelligence and Robotics from Sapienza University of Rome.
	Vincenzo Suriani earned his Ph.D. in Computer Engineering in 2024 from Sapienza University of Rome, with a specialization in artificial intelligence, robotic vision, and multi-agent coordination. Since 2016, he has served as Software Development Leader of the Sapienza Soccer Robot Team, contributing to major robotic competitions and international initiatives such as EUROBENCH, SciRoc, and Tech4YOU. He is currently a Research Fellow at the University of Basilicata, where he focuses on developing intelligent environments for software testing automation. His research, recognized with award-winning papers at the RoboCup International Symposium (2021, 2023, 2025), centers on robotic semantic mapping, object recognition, and human–robot interaction.
	Domenico Daniele Bloisi is an associate professor of Artificial Intelligence at the International University of Rome UNINT. Previously, he was associate professor at the University of Basilicata, assistant professor at the University of Verona, and assistant professor at Sapienza University of Rome. He received his PhD, master’s and bachelor’s degrees in Computer Engineering from Sapienza University of Rome in 2010, 2006 and 2004, respectively. He is the author of more than 80 peer-reviewed papers published in international journals and conferences in the field of artificial intelligence and robotics, with a focus on image analysis, multi-robot coordination, visual perception and information fusion. Dr. Bloisi conducts research in the field of melanoma and oral carcinoma prevention through automatic medical image analysis in collaboration with specialized medical teams in Italy. In addition, Dr. Bloisi is WP3 leader of the EU H2020 SOLARIS project, unit leader for the PRIN PNRR RETINA project, unit leader for the PRIN 2022 AIDA project. Since 2015, he is the team manager of the SPQR robot soccer team participating in the RoboCup world competitions
	Can Lin is a master student in Data Science at Sapienza university of Rome. He holds a bachelor degree in Computer science and Artificial intelligence from the same university. He joined the SPQR team in September of 2024, focusing on tasks related to computer vision.

The video could highlight your own research, that of another researcher or group, introduce viewers to an AI topic, or include interviews with AI researchers. Any theme is welcome, but videos covering the following are particularly encouraged: large language models, AI and ethics, societal impact of AI, and risks of deployed AI.

The videos will be assessed in terms of their content, understandability, relevance to AI, entertainment value, and presentation quality.

To give you a flavour of what these videos could look like, click here to see the winners of the previous iteration of the competition.

The deadline for submissions is 30 November 2025. To submit, you must upload your video to a publicly accessible website, and fill in the submission form.

You can find out more here.

Drones and Droids is a co-operative strategy game developed here at the Scottish Association for Marine Science (SAMS). In it, you and four other sentient beings set out on the deck of the research vessel Seol Mara with six of our best robots to investigate an algal bloom near Lismore. Your mission is to find the source of the bloom and determine if it is dangerous before it reaches the seafarms in Ardmucknish bay. The mechanics of the game are designed to reflect the behaviour of our robots, and the narrative of your mission is told as you explore the map and deal with various calamities drawn from the real-life experiences of the roboteers, phycologists and other scientists at SAMS.

We originally designed it as a teaching tool, but after good reviews from our local pro-gamers we’re currently running a crowdfunding campaign to allow us to do a full production run. There’s a little over a month to go and we’re half-way to our goal of £16000. If we can reach it, we’ll be able to give a copy to every school in Argyll, and have plenty left over to sell, supporting our research. If you have an interest in robotics, abyssal horrors or teaching the next generation of scientists and technicians about these subjects, please consider donating, and keep an eye on the Drones and Droids website for updates.

By Silvia Cernea Clark

Researchers at Rice University have developed a soft robotic arm capable of performing complex tasks such as navigating around an obstacle or hitting a ball, guided and powered remotely by laser beams without any onboard electronics or wiring. The research could inform new ways to control implantable surgical devices or industrial machines that need to handle delicate objects.

In a proof-of-concept study that integrates smart materials, machine learning and an optical control system, a team of Rice researchers led by materials scientist Hanyu Zhu used a light-patterning device to precisely induce motion in a robotic arm made from azobenzene liquid crystal elastomer ⎯ a type of polymer that responds to light.

According to the study published in Advanced Intelligent Systems, the new robotic system incorporates a neural network trained to predict the exact light pattern needed to create specific arm movements. This makes it easier for the robot to execute complex tasks without needing similarly complex input from an operator.

“This was the first demonstration of real-time, reconfigurable, automated control over a light-responsive material for a soft robotic arm,” said Elizabeth Blackert, a Rice doctoral alumna who is the first author on the study.

Elizabeth Blackert and Hanyu Zhu (Photo credit: Jeff Fitlow/Rice University).

Conventional robots typically involve rigid structures with mobile elements like hinges, wheels or grippers to enable a predefined, relatively constrained range of motion. Soft robots have opened up new areas of application in contexts like medicine, where safely interacting with delicate objects is required. So-called continuum robots are a type of soft robot that forgoes mobility constraints, enabling adaptive motion with a vastly expanded degree of freedom.

“A major challenge in using soft materials for robots is they are either tethered or have very simple, predetermined functionality,” said Zhu, assistant professor of materials science and nanoengineering. “Building remotely and arbitrarily programmable soft robots requires a unique blend of expertise involving materials development, optical system design and machine learning capabilities. Our research team was uniquely suited to take on this interdisciplinary work.”

The team created a new variation of an elastomer that shrinks under blue laser light then relaxes and regrows in the dark ⎯ a feature known as fast relaxation time that makes real-time control possible. Unlike other light-sensitive materials that require harmful ultraviolet light or take minutes to reset, this one works with safer, longer wavelengths and responds within seconds.

“When we shine a laser on one side of the material, the shrinking causes the material to bend in that direction,” Blackert said. “Our material bends toward laser light like a flower stem does toward sunlight.”

To control the material, the researchers used a spatial light modulator to split a single laser beam into multiple beamlets, each directed to a different part of the robotic arm. The beamlets can be turned on or off and adjusted in intensity, allowing the arm to bend or contract at any given point, much like the tentacles of an octopus. This technique can in principle create a robot with virtually infinite degrees of freedom ⎯ far beyond the capabilities of traditional robots with fixed joints.

“What is new here is using the light pattern to achieve complex changes in shape,” said Rafael Verduzco, professor and associate chair of chemical and biomolecular engineering and professor of materials science and nanoengineering. “In prior work, the material itself was patterned or programmed to change shape in one way, but here the material can change in multiple ways, depending on the laser beamlet pattern.”

To train such a multiparameter arm, the team ran a small number of combinations of light settings and recorded how the robot arm deformed in each case, using the data to train a convolutional neural network ⎯ a type of artificial intelligence used in image recognition. The model was then able to output the exact light pattern needed to create a desired shape such as flexing or a reach-around motion.

The current prototype is flat and moves in 2D, but future versions could bend in three dimensions with additional sensors and cameras.

Photo credit: Jeff Fitlow/Rice University

“This is a step towards having safer, more capable robotics for various applications ranging from implantable biomedical devices to industrial robots that handle soft goods,” Blackert said.

By Ari Koeppel, Dartmouth College

NASA’s search for evidence of past life on Mars just produced an exciting update. On Sept. 10, 2025, a team of scientists published a paper detailing the Perseverance rover’s investigation of a distinctive rock outcrop called Bright Angel on the edge of Mars’ Jezero Crater. This outcrop is notable for its light-toned rocks with striking mineral nodules and multicolored, leopard print-like splotches.

By combining data from five scientific instruments, the team determined that these nodules formed through processes that could have involved microorganisms. While this finding is not direct evidence of life, it’s a compelling discovery that planetary scientists hope to look into more closely.

Bright Angel rock surface at the Beaver Falls site on Mars shows nodules on the right and a leopard-like pattern at the center. NASA/JPL-Caltech/MSSS

To appreciate how discoveries like this one come about, it’s helpful to understand how scientists engage with rover data — that is, how planetary scientists like me use robots like Perseverance on Mars as extensions of our own senses.

Experiencing Mars through data

When you strap on a virtual reality headset, you suddenly lose your orientation to the immediate surroundings, and your awareness is transported by light and sound to a fabricated environment. For Mars scientists working on rover mission teams, something very similar occurs when rovers send back their daily downlinks of data.

Several developers, including MarsVR, Planetary Visor and Access Mars, have actually worked to build virtual Mars environments for viewing with a virtual reality headset. However, much of Mars scientists’ daily work instead involves analyzing numerical data visualized in graphs and plots. These datasets, produced by state-of-the-art sensors on Mars rovers, extend far beyond human vision and hearing.

A virtual Mars environment developed by Planetary Visor incorporates both 3D landscape data and rover instrument data as pop-up plots. Scientists typically access data without entering a virtual reality space. However, tools like this give the public a sense for how mission scientists experience their work.

Developing an intuition for interpreting these complex datasets takes years, if not entire careers. It is through this “mind-data connection” that scientists build mental models of Martian landscapes – models they then communicate to the world through scientific publications.

The robots’ tool kit: Sensors and instruments

Five primary instruments on Perseverance, aided by machine learning algorithms, helped describe the unusual rock formations at a site called Beaver Falls and the past they record.

Robotic hands: Mounted on the rover’s robotic arm are tools for blowing dust aside and abrading rock surfaces. These ensure the rover analyzes clean samples.

Cameras: Perseverance hosts 19 cameras for navigation, self-inspection and science. Five science-focused cameras played a key role in this study. These cameras captured details unseeable by human eyes, including magnified mineral textures and light in infrared wavelengths. Their images revealed that Bright Angel is a mudstone, a type of sedimentary rock formed from fine sediments deposited in water.

Spectrometers: Instruments such as SuperCam and SHERLOC – scanning habitable environments with Raman and luminescence for organics and chemicals – analyze how rocks reflect or emit light across a range of wavelengths. Think of this as taking hundreds of flash photographs of the same tiny spot, all in different “colors.” These datasets, called spectra, revealed signs of water integrated into mineral structures in the rock and traces of organic molecules: the basic building blocks of life.

Subsurface radar: RIMFAX, the radar imager for Mars subsurface experiment, uses radio waves to peer beneath Mars’ surface and map rock layers. At Beaver Falls, this showed the rocks were layered over other ancient terrains, likely due to the activity of a flowing river. Areas with persistently present water are better habitats for microbes than dry or intermittently wet locations.

X-ray chemistry: PIXL, the planetary instrument for X-ray lithochemistry, bombards rock surfaces with X-rays and observes how the rock glows or reflects them. This technique can tell researchers which elements and minerals the rock contains at a fine scale. PIXL revealed that the leopard-like spots found at Beaver Falls differed chemically from the surrounding rock. The spots resembled patterns on Earth formed by chemical reactions that are mediated by microbes underwater.

Key Perseverance Mars Rover instruments used in this analysis. NASA

Together, these instruments produce a multifaceted picture of the Martian environment. Some datasets require significant processing, and refined machine learning algorithms help the mission teams turn that information into a more intuitive description of the Jezero Crater’s setting, past and present.

The challenge of uncertainty

Despite Perseverance’s remarkable tools and processing software, uncertainty remains in the results. Science, especially when conducted remotely on another planet, is rarely black and white. In this case, the chemical signatures and mineral formations at Beaver Falls are suggestive – but not conclusive – of past life on Mars.

There actually are tools, such as mass spectrometers, that can show definitively whether a rock sample contains evidence of biological activity. However, these instruments are currently too fragile, heavy and power-intensive for Mars missions.

Fortunately, Perseverance has collected and sealed rock core samples from Beaver Falls and other promising sites in Jezero Crater with the goal of sending them back to Earth. If the current Mars sample return plan can retrieve these samples, laboratories on Earth can scrutinize them far more thoroughly than the rover was able to.

Perseverance selfie at Cheyava Falls sampling site in the Beaver Falls location. NASA/JPL-Caltech/MSSS

Investing in our robotic senses

This discovery is a testament to decades of NASA’s sustained investment in Mars exploration and the work of engineering teams that developed these instruments. Yet these investments face an uncertain future.

The White House’s budget office recently proposed cutting 47% of NASA’s science funding. Such reductions could curtail ongoing missions, including Perseverance’s continued operations, which are targeted for a 23% cut, and jeopardize future plans such as the Mars sample return campaign, among many other missions.

Perseverance represents more than a machine. It is a proxy extending humanity’s senses across millions of miles to an alien world. These robotic explorers and the NASA science programs behind them are a key part of the United States’ collective quest to answer profound questions about the universe and life beyond Earth.

Ari Koeppel, Earth Sciences Postdoctoral Scientist and Adjunct Associate, Dartmouth College

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Claire chatted to Frances Zhu from the Colorado School of Mines about intelligent robotic systems for space exploration.

Frances Zhu has a degree in Mechanical and Aerospace Engineering and a Ph.D. in Aerospace Engineering from Cornell University. She was previously a NASA Space Technology Research Fellow and an Assistant Research Professor in the Hawaii Institute of Geophysics and Planetology at the University of Hawaii, specialising in machine learning, dynamics, systems, and controls engineering. Since 2025, she has been an Assistant Professor at the Colorado School of Mines in the Department of Mechanical Engineering, affiliated with the Robotics program and Space Resources Program.

October 1 was International Women in Robotics Day, and we’re delighted to introduce this year’s edition of “Women in Robotics You Need to Know About”! Robotics is no longer confined to factories or research labs. Today, it’s helping us explore space, care for people, grow food, and connect across the globe. Behind these breakthroughs are women who lead research groups, launch startups, set safety standards, and inspire the next generation. Too often, their contributions remain under-recognized, and this list is one way we can make that work visible.

This year’s list highlights 20 women in robotics you need to know about in 2025. They are professors, engineers, founders, communicators, and project leaders. Some are early in their careers, others have already shaped the field for decades. Their work ranges from tactile sensing that gives robots a human-like sense of touch, to swarm robotics for medicine and the environment, to embodied AI that may one day live in our homes. They come from across the world, including Australia, Brazil, Canada, China, Germany, Spain, Switzerland, the United Kingdom, and the United States. Together, they show us just how wide the world of robotics really is.

We publish this list not only to celebrate their achievements, but also to counter the persistent invisibility of women in robotics. Representation matters. When women’s contributions are overlooked, we risk reinforcing the false perception that robotics is not their domain. The honorees you’ll meet here prove the opposite. They are making discoveries, leading teams, starting companies, writing the standards, and pushing the boundaries of what robots can do.

The 2025 honorees

• Heba Khamis
  Heba Khamis is lecturer at UNSW and co-founder of Contactile, start-up making tactile sensors that give robots a human sense of touch to enable them to perform difficult material handling tasks.
• Kelen Teixeira Vivaldini
  Kelen Teixeira Vivaldini is a professor at UFSCar, Brazil, researching autonomous robots, intelligent systems, and mission planning, with applications in environmental monitoring and inspection.
• Natalie Panek
  Natalie Panek is a senior engineer in systems design and works in the robotics and automation division of the space technology company, MDA. She was named on Forbes 2015 30 under 30 and one of WXN’s 2014 Top 100 award winners.
• Joelle Pineau
  Joelle Pineau is a Canadian AI researcher and robotics leader and a professor at McGill. She also served as Meta AI’s vice‑president until 2025. She co‑directs the Reasoning & Learning Lab, co‑founded SmartWheeler and Nursebot and champions reproducible research.
• Hallie Siegel
  Hallie Siegel is a science communicator and former Robohub editor, building global networks that connect researchers and the public.

• Xiaorui Zhu
  Xiaorui Zhu co-founded DJI and RoboSense and directs Galaxy AI & Robotics, with award-winning research in UAVs, autonomous driving and mobile robotics.
• Lijin Aryananda
  Lijin Aryananda is a robotics researcher with 15+ years’ experience in humanoids, automation & medical devices. At ZHAW she develops AI methods for tomography, bridging academia & industry with inclusive leadership.
• Georgia Chalvatzaki
  Georgia Chalvatzaki, professor at TU Darmstadt and head of the PEARL Lab, advances human-centric robot learning. Her work blends AI and robotics to give mobile manipulators the ability to collaborate safely and intelligently with people.
• Mar Masulli
  Mar Masulli is CEO & co-founder of BitMetrics, using AI to give robots & machines vision and reasoning. She also serves on the Spanish Robotics Association board.
• Alona Kharchenko
  Alona Kharchenko is co-founder & CTO of Devanthro, building embodied AI for homes, and was recognized by Forbes as 30 Under 30.

• Nicole Robinson
  Nicole Robinson is the co-founder of Lyro Robotics, deploying AI pick-and-pack robots for industry.
• Dimitra Gkatzia
  Dimitra Gkatzia is an associate professor at Edinburgh Napier, advancing natural language generation for human-robot interaction.
• Sabine Hauert
  Sabine Hauert is a professor at the University of Bristol, co-founder of Robohub, and a pioneer in swarm robotics for nanomedicine and the environment.
• Monica Anderson
  Monica Anderson is a professor at the University of Alabama, researching distributed autonomy and inclusive human-robot teaming.
• Shilpa Gulati
  Shilpa Gulati is an experienced engineering leader with over 15 years of experience in building and scaling teams to solve complex problems in Robotics using state-of-the-art technologies.

• Shuran Song
  Shuran Song is a Stanford professor and robotics researcher, building low-cost systems for robot perception and releasing influential open datasets.
• Kathryn Zealand
  Kathryn Zealand is co-founder of Skip, building powered clothing, “e-bikes for walking”. She spun the project out of X and has a background in theoretical physics.
• Ann Virts
  Ann Virts is a NIST project leader developing test methods for mobile and wearable robots, recognized with a U.S. DOC Bronze Medal.
• Carole Franklin
  Carole Franklin directs standards development for robotics at the Association for Advancing Automation (A3), leading ANSI & ISO robot safety work. With a background at Booz Allen & Ford, she champions safe, effective deployment of robots.
• Meghan Daley
  Meghan Daley is a NASA project manager who leads teams to develop and integrate simulations for robotic operations to prepare astronauts on the ISS and beyond.

We’ll be spotlighting five honorees each week throughout October, so stay tuned for deeper profiles and stories of their work.

Why it matters

Robotics is not just about technology; it’s about people. By showcasing these individuals, we hope to inspire the next generation, connect the community, and advance the values of diversity and inclusion in STEM

Join the conversation on social media with #WomenInRobotics and help us celebrate the people making robotics better for everyone.

The article was cross-posted from Women in Robotics. Read the original here.

Tell us a bit about your PhD – where are you studying, and what is the topic of your research?

I am pursuing my PhD at Purdue University, where my dissertation focuses on developing scalable and adaptive deep learning frameworks for human activity recognition (HAR) using wearable sensors. I was drawn to this topic because wearables have the potential to transform fields like healthcare, elderly care, and long-term activity tracking. Unlike video-based recognition, which can raise privacy concerns and requires fixed camera setups, wearables are portable, non-intrusive, and capable of continuous monitoring, making them ideal for capturing activity data in natural, real-world settings.

The central challenge my dissertation addresses is that wearable data is often noisy, inconsistent, and uncertain, depending on sensor placement, movement artifacts, and device limitations. My goal is to design deep learning models that are not only computationally efficient and interpretable but also robust to the variability of real-world data. In doing so, I aim to ensure that wearable HAR systems are both practical and trustworthy for deployment outside controlled lab environments.

This research has been supported by the Polytechnic Summer Research Grant at Purdue. Beyond my dissertation work, I contribute to the research community as a reviewer for conferences such as CoDIT, CTDIAC, and IRC, and I have been invited to review for AAAI 2026. I was also involved in community building, serving as Local Organizer and Safety Chair for the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), and continuing as Safety Chair for AAMAS 2026.

Could you give us an overview of the research you’ve carried out so far during your PhD?

So far, my research has focused on developing a hierarchical fuzzy deep neural network that can adapt to diverse human activity recognition datasets. In my initial work, I explored a hierarchical recognition approach, where simpler activities are detected at earlier levels of the model and more complex activities are recognized at higher levels. To enhance both robustness and interpretability, I integrated fuzzy logic principles into deep learning, allowing the model to better handle uncertainty in real-world sensor data.

A key strength of this model is its simplicity and low computational cost, which makes it particularly well suited for real-time activity recognition on wearable devices. I have rigorously evaluated the framework on multiple benchmark datasets of multivariate time series and systematically compared its performance against state-of-the-art methods, where it has demonstrated both competitive accuracy and improved interpretability.

Is there an aspect of your research that has been particularly interesting?

Yes, what excites me most is discovering how different approaches can make human activity recognition both smarter and more practical. For instance, integrating fuzzy logic has been fascinating, because it allows the model to capture the natural uncertainty and variability of human movement. Instead of forcing rigid classifications, the system can reason in terms of degrees of confidence, making it more interpretable and closer to how humans actually think.

I also find the hierarchical design of my model particularly interesting. Recognizing simple activities first, and then building toward more complex behaviors, mirrors the way humans often understand actions in layers. This structure not only makes the model efficient but also provides insights into how different activities relate to one another.

Beyond methodology, what motivates me is the real-world potential. The fact that these models can run efficiently on wearables means they could eventually support personalized healthcare, elderly care, and long term activity monitoring in people’s everyday lives. And since the techniques I’m developing apply broadly to time series data, their impact could extend well beyond HAR, into areas like medical diagnostics, IoT monitoring, or even audio recognition. That sense of both depth and versatility is what makes the research especially rewarding for me.

What are your plans for building on your research so far during the PhD – what aspects will you be investigating next?

Moving forward, I plan to further enhance the scalability and adaptability of my framework so that it can effectively handle large scale datasets and support real-time applications. A major focus will be on improving both the computational efficiency and interpretability of the model, ensuring it is not only powerful but also practical for deployment in real-world scenarios.

While my current research has focused on human activity recognition, I am excited to broaden the scope to the wider domain of time series classification. I see great potential in applying my framework to areas such as sound classification, physiological signal analysis, and other time-dependent domains. This will allow me to demonstrate the generalizability and robustness of my approach across diverse applications where time-based data is critical.

In the longer term, my goal is to develop a unified, scalable model for time series analysis that balances adaptability, interpretability, and efficiency. I hope such a framework can serve as a foundation for advancing not only HAR but also a broad range of healthcare, environmental, and AI-driven applications that require real-time, data-driven decision-making.

What made you want to study AI, and in particular the area of wearables?

My interest in wearables began during my time in Paris, where I was first introduced to the potential of sensor-based monitoring in healthcare. I was immediately drawn to how discreet and non-invasive wearables are compared to video-based methods, especially for applications like elderly care and patient monitoring.

More broadly, I have always been fascinated by AI’s ability to interpret complex data and uncover meaningful patterns that can enhance human well-being. Wearables offered the perfect intersection of my interests, combining cutting-edge AI techniques with practical, real-world impact, which naturally led me to focus my research on this area.

What advice would you give to someone thinking of doing a PhD in the field?

A PhD in AI demands both technical expertise and resilience. My advice would be:

• Stay curious and adaptable, because research directions evolve quickly, and the ability to pivot or explore new ideas is invaluable.
• Investigate combining disciplines. AI benefits greatly from insights in fields like psychology, healthcare, and human-computer interaction.
• Most importantly, choose a problem you are truly passionate about. That passion will sustain you through the inevitable challenges and setbacks of the PhD journey.

Approaching your research with curiosity, openness, and genuine interest can make the PhD not just a challenge, but a deeply rewarding experience.

Could you tell us an interesting (non-AI related) fact about you?

Outside of research, I’m passionate about leadership and community building. As president of the Purdue Tango Club, I grew the group from just 2 students to over 40 active members, organized weekly classes, and hosted large events with internationally recognized instructors. More importantly, I focused on creating a welcoming community where students feel connected and supported. For me, tango is more than dance, it’s a way to bring people together, bridge cultures, and balance the intensity of research with creativity and joy.

I also apply these skills in academic leadership. For example, I serve as Local Organizer and Safety Chair for the AAMAS 2025 and 2026 conferences, which has given me hands-on experience managing events, coordinating teams, and creating inclusive spaces for researchers worldwide.

About Zahra

Zahra Ghorrati is a PhD candidate and teaching assistant at Purdue University, specializing in artificial intelligence and time series classification with applications in human activity recognition. She earned her undergraduate degree in Computer Software Engineering and her master’s degree in Artificial Intelligence. Her research focuses on developing scalable and interpretable fuzzy deep learning models for wearable sensor data. She has presented her work at leading international conferences and journals, including AAMAS, PAAMS, FUZZ-IEEE, IEEE Access, System and Applied Soft Computing. She has served as a reviewer for CoDIT, CTDIAC, and IRC, and has been invited to review for AAAI 2026. Zahra also contributes to community building as Local Organizer and Safety Chair for AAMAS 2025 and 2026.

Claire chatted to Ali K. Hoshiar from University of Essex about how microrobots move and work together.

Ali Hoshiar is a Senior Lecturer in Robotics at the University of Essex and Director of the Robotics for Under Millimetre Innovation (RUMI) Lab. He leads the EPSRC-funded ‘In-Target’ project and was awarded the university’s Best Interdisciplinary Research Award. His research focuses on microrobotics, soft robotics, and data-driven mechatronic systems for medical and agri-tech applications. He also holds an MBA, adding strategic and commercial insight to his technical work.

By Alyssa Schaechinger

While working at NASA in 2003, Dr. Robert Ambrose, director of the Robotics and Automation Design Lab (RAD Lab), designed a robot with no fixed top or bottom. A perfect sphere, the RoboBall could not flip over, and its shape promised access to places wheeled or legged machines could not reach — from the deepest lunar crater to the uneven sands of a beach. Two of his students built the first prototype, but then Ambrose shelved the idea to focus on drivable rovers for astronauts.

When Ambrose arrived at Texas A&M University in 2021, he saw a chance to reignite his idea. With funding from the Chancellor’s Research Initiative and Governor’s University Research Initiative, Ambrose brought RoboBall back to life.

Now, two decades after the original idea, RoboBall is rolling across Texas A&M University.

Driven by graduate students Rishi Jangale and Derek Pravecek, the RAD Lab is intent on sending RoboBall, a novel spherical robot, into uncharted terrain.

Jangale and Pravecek, both Ph.D. students in the J. Mike Walker ’66 Department of Mechanical Engineering, have played a significant part in getting the ball rolling once again.

“Dr. Ambrose has given us such a cool opportunity. He gives us the chance to work on RoboBall however we want,” said Jangale, who began work on RoboBall in 2022. “We manage ourselves, and we get to take RoboBall in any direction we want.”

Pravecek echoed that sense of freedom. “We get to work as actual engineers doing engineering tasks. This research teaches us things beyond what we read in textbooks,” he said. “It really is the best of both worlds.”

Robot in an airbag

At the heart of the project is the simple concept of a “robot in an airbag.” Two versions now exist in tandem. RoboBall II, a 2-foot-diameter prototype, is tuned for trial runs, monitoring power output and control algorithms. RoboBall III has a diameter of 6 feet across and is built with plans to carry payloads such as sensors, cameras or sampling tools, for real-world missions.

Upcoming tests will continue to take RoboBall into outdoor environments. RAD Lab researchers are planning field trials on the beaches of Galveston to demonstrate a water-to-land transition, testing the robot’s buoyancy and terrain adaptability in a real-world setting.

“Traditional vehicles stall or tip over in abrupt transitions,” Jangale explained. “This robot can roll out of water onto sand without worrying about orientation. It’s going where other robots can’t.”

The factors that create the versatility of RoboBall also lead to some of its challenges. Once sealed inside its protective shell, the robot can only be accessed electronically. Any mechanical failure means disassembly and digging through layers of wiring and actuators.

“Diagnostics can be a headache,” said Pravacek. “If a motor fails or a sensor disconnects, you can’t just pop open a panel. You have to take apart the whole robot and rebuild. It’s like open-heart surgery on a rolling ball.”

RoboBall’s novelty means the team often operates without a blueprint.

“Every task is new,” Jangale said. “We’re very much on our own. There’s no literature on soft-shelled spherical robots of this size that roll themselves.”

Despite those hurdles, the students find themselves surprised every time the robot outperforms expectations.

“When it does something we didn’t think was possible, I’m always surprised,” Pravecek said. “It still feels like magic.”

Student-led innovation

The team set a new record when RoboBall II reached 20 miles per hour, roughly half its theoretical power output. “We didn’t anticipate hitting that speed so soon,” Pravecek said. “It was thrilling, and it opened up new targets. Now we’re pushing even further.”

Ambrose sees these reactions as proof that student-led innovation thrives when engineers have room to explore.

“The autonomy Rishi and Derek have is exactly what a project like this needs,” he said. “They’re not just following instructions — they’re inventing the next generation of exploration tools.”

Long-term goals include autonomous navigation and remote deployment. The team hopes to see RoboBall dispatched from a lunar lander to chart steep crater walls or launched from an unmanned drone to survey post-disaster landscapes on Earth. Each ball could map terrain, transmit data back to operators and even deploy instruments in hard-to-reach spots.

“Imagine a swarm of these balls deployed after a hurricane,” Jangale said. “They could map flooded areas, find survivors and bring back essential data — all without risking human lives.”

As the RoboBall project rolls on, student-driven research stands on full display.

“Engineering is problem solving at its purest,” Ambrose said. “Give creative minds a challenge and the freedom to explore, and you’ll see innovation roll into reality.”

Claire chatted to Yuen Ting Chan from Natural History Museum about using robots to automate molecular biology experiments.

Yuen Ting Chan has nearly 20 years of experience working on translating, developing and optimising laboratory protocols, from DNA forensics to the biomedical field. She has brought automation to molecular laboratories for over 12 years, translating the laboratory protocols into bespoke scripts for a wide variety of liquid handling instruments. Her role at the Natural History Museum is to bring automation to the molecular laboratories, thus providing more opportunities for researchers to work on projects with large sample numbers for the wide variety of specimens within the museum.

Claire chatted to competitors, exhibitors, and attendees at the Smart City Robotics Competition in Milton Keynes.

This bonus episode was sponsored by euRobotics, an international non-profit association that aims to boost European robotics research, development, and innovation.

Claire chatted to Chad Jenkins from University of Michigan about how robots can learn from people and assist us in our daily lives.

Odest Chadwicke Jenkins is a Professor of Robotics and a Professor of Electrical Engineering and Computer Science at the University of Michigan. His research addresses problems in robot learning from demonstration and human-robot interaction, primarily focused on dexterous mobile manipulation and robot perception. In 2022, he founded the Robotics Major Degree Program for undergraduates at the University of Michigan. He was awarded the 2024 ACM/CMD-IT Richard A. Tapia Achievement Award for Scientific Scholarship, Civic Science, and Diversifying Computing.

Photo credit: Joseph Xu, Michigan Engineering Communications & Marketing

By Alex Shipps

Chatbots like ChatGPT and Claude have experienced a meteoric rise in usage over the past three years because they can help you with a wide range of tasks. Whether you’re writing Shakespearean sonnets, debugging code, or need an answer to an obscure trivia question, artificial intelligence systems seem to have you covered. The source of this versatility? Billions, or even trillions, of textual data points across the internet.

Those data aren’t enough to teach a robot to be a helpful household or factory assistant, though. To understand how to handle, stack, and place various arrangements of objects across diverse environments, robots need demonstrations. You can think of robot training data as a collection of how-to videos that walk the systems through each motion of a task. Collecting these demonstrations on real robots is time-consuming and not perfectly repeatable, so engineers have created training data by generating simulations with AI (which don’t often reflect real-world physics), or tediously handcrafting each digital environment from scratch.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Toyota Research Institute may have found a way to create the diverse, realistic training grounds robots need. Their “steerable scene generation” approach creates digital scenes of things like kitchens, living rooms, and restaurants that engineers can use to simulate lots of real-world interactions and scenarios. Trained on over 44 million 3D rooms filled with models of objects such as tables and plates, the tool places existing assets in new scenes, then refines each one into a physically accurate, lifelike environment.

Steerable scene generation creates these 3D worlds by “steering” a diffusion model — an AI system that generates a visual from random noise — toward a scene you’d find in everyday life. The researchers used this generative system to “in-paint” an environment, filling in particular elements throughout the scene. You can imagine a blank canvas suddenly turning into a kitchen scattered with 3D objects, which are gradually rearranged into a scene that imitates real-world physics. For example, the system ensures that a fork doesn’t pass through a bowl on a table — a common glitch in 3D graphics known as “clipping,” where models overlap or intersect.

How exactly steerable scene generation guides its creation toward realism, however, depends on the strategy you choose. Its main strategy is “Monte Carlo tree search” (MCTS), where the model creates a series of alternative scenes, filling them out in different ways toward a particular objective (like making a scene more physically realistic, or including as many edible items as possible). It’s used by the AI program AlphaGo to beat human opponents in Go (a game similar to chess), as the system considers potential sequences of moves before choosing the most advantageous one.

“We are the first to apply MCTS to scene generation by framing the scene generation task as a sequential decision-making process,” says MIT Department of Electrical Engineering and Computer Science (EECS) PhD student Nicholas Pfaff, who is a CSAIL researcher and a lead author on a paper presenting the work. “We keep building on top of partial scenes to produce better or more desired scenes over time. As a result, MCTS creates scenes that are more complex than what the diffusion model was trained on.”

In one particularly telling experiment, MCTS added the maximum number of objects to a simple restaurant scene. It featured as many as 34 items on a table, including massive stacks of dim sum dishes, after training on scenes with only 17 objects on average.

Steerable scene generation also allows you to generate diverse training scenarios via reinforcement learning — essentially, teaching a diffusion model to fulfill an objective by trial-and-error. After you train on the initial data, your system undergoes a second training stage, where you outline a reward (basically, a desired outcome with a score indicating how close you are to that goal). The model automatically learns to create scenes with higher scores, often producing scenarios that are quite different from those it was trained on.

Users can also prompt the system directly by typing in specific visual descriptions (like “a kitchen with four apples and a bowl on the table”). Then, steerable scene generation can bring your requests to life with precision. For example, the tool accurately followed users’ prompts at rates of 98 percent when building scenes of pantry shelves, and 86 percent for messy breakfast tables. Both marks are at least a 10 percent improvement over comparable methods like “MiDiffusion” and “DiffuScene.”

The system can also complete specific scenes via prompting or light directions (like “come up with a different scene arrangement using the same objects”). You could ask it to place apples on several plates on a kitchen table, for instance, or put board games and books on a shelf. It’s essentially “filling in the blank” by slotting items in empty spaces, but preserving the rest of a scene.

According to the researchers, the strength of their project lies in its ability to create many scenes that roboticists can actually use. “A key insight from our findings is that it’s OK for the scenes we pre-trained on to not exactly resemble the scenes that we actually want,” says Pfaff. “Using our steering methods, we can move beyond that broad distribution and sample from a ‘better’ one. In other words, generating the diverse, realistic, and task-aligned scenes that we actually want to train our robots in.”

Such vast scenes became the testing grounds where they could record a virtual robot interacting with different items. The machine carefully placed forks and knives into a cutlery holder, for instance, and rearranged bread onto plates in various 3D settings. Each simulation appeared fluid and realistic, resembling the real-world, adaptable robots steerable scene generation could help train, one day.

While the system could be an encouraging path forward in generating lots of diverse training data for robots, the researchers say their work is more of a proof of concept. In the future, they’d like to use generative AI to create entirely new objects and scenes, instead of using a fixed library of assets. They also plan to incorporate articulated objects that the robot could open or twist (like cabinets or jars filled with food) to make the scenes even more interactive.

To make their virtual environments even more realistic, Pfaff and his colleagues may incorporate real-world objects by using a library of objects and scenes pulled from images on the internet and using their previous work on “Scalable Real2Sim.” By expanding how diverse and lifelike AI-constructed robot testing grounds can be, the team hopes to build a community of users that’ll create lots of data, which could then be used as a massive dataset to teach dexterous robots different skills.

“Today, creating realistic scenes for simulation can be quite a challenging endeavor; procedural generation can readily produce a large number of scenes, but they likely won’t be representative of the environments the robot would encounter in the real world. Manually creating bespoke scenes is both time-consuming and expensive,” says Jeremy Binagia, an applied scientist at Amazon Robotics who wasn’t involved in the paper. “Steerable scene generation offers a better approach: train a generative model on a large collection of pre-existing scenes and adapt it (using a strategy such as reinforcement learning) to specific downstream applications. Compared to previous works that leverage an off-the-shelf vision-language model or focus just on arranging objects in a 2D grid, this approach guarantees physical feasibility and considers full 3D translation and rotation, enabling the generation of much more interesting scenes.”

“Steerable scene generation with post training and inference-time search provides a novel and efficient framework for automating scene generation at scale,” says Toyota Research Institute roboticist Rick Cory SM ’08, PhD ’10, who also wasn’t involved in the paper. “Moreover, it can generate ‘never-before-seen’ scenes that are deemed important for downstream tasks. In the future, combining this framework with vast internet data could unlock an important milestone towards efficient training of robots for deployment in the real world.”

Pfaff wrote the paper with senior author Russ Tedrake, the Toyota Professor of Electrical Engineering and Computer Science, Aeronautics and Astronautics, and Mechanical Engineering at MIT; a senior vice president of large behavior models at the Toyota Research Institute; and CSAIL principal investigator. Other authors were Toyota Research Institute robotics researcher Hongkai Dai SM ’12, PhD ’16; team lead and Senior Research Scientist Sergey Zakharov; and Carnegie Mellon University PhD student Shun Iwase. Their work was supported, in part, by Amazon and the Toyota Research Institute. The researchers presented their work at the Conference on Robot Learning (CoRL) in September.

By Corey Zheng, Georgia Institute of Technology and Shu Jia, Georgia Institute of Technology

Inspired by the human eye, our biomedical engineering lab at Georgia Tech has designed an adaptive lens made of soft, light-responsive, tissuelike materials.

Adjustable camera systems usually require a set of bulky, moving, solid lenses and a pupil in front of a camera chip to adjust focus and intensity. In contrast, human eyes perform these same functions using soft, flexible tissues in a highly compact form.

Our lens, called the photo-responsive hydrogel soft lens, or PHySL, replaces rigid components with soft polymers acting as artificial muscles. The polymers are composed of a hydrogel − a water-based polymer material. This hydrogel muscle changes the shape of a soft lens to alter the lens’s focal length, a mechanism analogous to the ciliary muscles in the human eye.

The hydrogel material contracts in response to light, allowing us to control the lens without touching it by projecting light onto its surface. This property also allows us to finely control the shape of the lens by selectively illuminating different parts of the hydrogel. By eliminating rigid optics and structures, our system is flexible and compliant, making it more durable and safer in contact with the body.

Why it matters

Artificial vision using cameras is commonplace in a variety of technological systems, including robots and medical tools. The optics needed to form a visual system are still typically restricted to rigid materials using electric power. This limitation presents a challenge for emerging fields, including soft robotics and biomedical tools that integrate soft materials into flexible, low-power and autonomous systems. Our soft lens is particularly suitable for this task.

Soft robots are machines made with compliant materials and structures, taking inspiration from animals. This additional flexibility makes them more durable and adaptive. Researchers are using the technology to develop surgical endoscopes, grippers for handling delicate objects and robots for navigating environments that are difficult for rigid robots.

The same principles apply to biomedical tools. Tissuelike materials can soften the interface between body and machine, making biomedical tools safer by making them move with the body. These include skinlike wearable sensors and hydrogel-coated implants.

What other research is being done in this field

This work merges concepts from tunable optics and soft “smart” materials. While these materials are often used to create soft actuators – parts of machines that move – such as grippers or propulsors, their application in optical systems has faced challenges.

Many existing soft lens designs depend on liquid-filled pouches or actuators requiring electronics. These factors can increase complexity or limit their use in delicate or untethered systems. Our light-activated design offers a simpler, electronics-free alternative.

What’s next

We aim to improve the performance of the system using advances in hydrogel materials. New research has yielded several types of stimuli-responsive hydrogels with faster and more powerful contraction abilities. We aim to incorporate the latest material developments to improve the physical capabilities of the photo-responsive hydrogel soft lens.

We also aim to show its practical use in new types of camera systems. In our current work, we developed a proof-of-concept, electronics-free camera using our soft lens and a custom light-activated, microfluidic chip. We plan to incorporate this system into a soft robot to give it electronics-free vision. This system would be a significant demonstration for the potential of our design to enable new types of soft visual sensing.

The Research Brief is a short take on interesting academic work.

Corey Zheng, PhD Student in Biomedical Engineering, Georgia Institute of Technology and Shu Jia, Assistant Professor of Biomedical Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Claire chatted to Edith-Clare Hall from the Advanced Research and Invention Agency (ARIA) about accelerating scientific and technological breakthroughs.

Edith-Clare Hall is a PhD student at the University of Bristol, Frontier Specialist at ARIA, and leader of Women in Robotics UK. She focuses on the critical interfaces where interconnected systems meet, working to close the gap between academic research and real-world deployment to unlock cyber-physical autonomy. At ARIA, she works as a technical generalist, accelerating breakthroughs across emerging and future programmes. Her PhD research focussed on creating bespoke robotic systems that deliver support for people with progressive conditions such as motor neurone disease (MND).

By Adam Zewe

A robot searching for workers trapped in a partially collapsed mine shaft must rapidly generate a map of the scene and identify its location within that scene as it navigates the treacherous terrain.

Researchers have recently started building powerful machine-learning models to perform this complex task using only images from the robot’s onboard cameras, but even the best models can only process a few images at a time. In a real-world disaster where every second counts, a search-and-rescue robot would need to quickly traverse large areas and process thousands of images to complete its mission.

To overcome this problem, MIT researchers drew on ideas from both recent artificial intelligence vision models and classical computer vision to develop a new system that can process an arbitrary number of images. Their system accurately generates 3D maps of complicated scenes like a crowded office corridor in a matter of seconds. 

The AI-driven system incrementally creates and aligns smaller submaps of the scene, which it stitches together to reconstruct a full 3D map while estimating the robot’s position in real-time.

Unlike many other approaches, their technique does not require calibrated cameras or an expert to tune a complex system implementation. The simpler nature of their approach, coupled with the speed and quality of the 3D reconstructions, would make it easier to scale up for real-world applications.

Beyond helping search-and-rescue robots navigate, this method could be used to make extended reality applications for wearable devices like VR headsets or enable industrial robots to quickly find and move goods inside a warehouse.

“For robots to accomplish increasingly complex tasks, they need much more complex map representations of the world around them. But at the same time, we don’t want to make it harder to implement these maps in practice. We’ve shown that it is possible to generate an accurate 3D reconstruction in a matter of seconds with a tool that works out of the box,” says Dominic Maggio, an MIT graduate student and lead author of a paper on this method.

Maggio is joined on the paper by postdoc Hyungtae Lim and senior author Luca Carlone, associate professor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), principal investigator in the Laboratory for Information and Decision Systems (LIDS), and director of the MIT SPARK Laboratory. The research will be presented at the Conference on Neural Information Processing Systems.

Mapping out a solution

For years, researchers have been grappling with an essential element of robotic navigation called simultaneous localization and mapping (SLAM). In SLAM, a robot recreates a map of its environment while orienting itself within the space.

Traditional optimization methods for this task tend to fail in challenging scenes, or they require the robot’s onboard cameras to be calibrated beforehand. To avoid these pitfalls, researchers train machine-learning models to learn this task from data.

While they are simpler to implement, even the best models can only process about 60 camera images at a time, making them infeasible for applications where a robot needs to move quickly through a varied environment while processing thousands of images.

To solve this problem, the MIT researchers designed a system that generates smaller submaps of the scene instead of the entire map. Their method “glues” these submaps together into one overall 3D reconstruction. The model is still only processing a few images at a time, but the system can recreate larger scenes much faster by stitching smaller submaps together.

“This seemed like a very simple solution, but when I first tried it, I was surprised that it didn’t work that well,” Maggio says.

Searching for an explanation, he dug into computer vision research papers from the 1980s and 1990s. Through this analysis, Maggio realized that errors in the way the machine-learning models process images made aligning submaps a more complex problem.

Traditional methods align submaps by applying rotations and translations until they line up. But these new models can introduce some ambiguity into the submaps, which makes them harder to align. For instance, a 3D submap of a one side of a room might have walls that are slightly bent or stretched. Simply rotating and translating these deformed submaps to align them doesn’t work.

“We need to make sure all the submaps are deformed in a consistent way so we can align them well with each other,” Carlone explains.

A more flexible approach

Borrowing ideas from classical computer vision, the researchers developed a more flexible, mathematical technique that can represent all the deformations in these submaps. By applying mathematical transformations to each submap, this more flexible method can align them in a way that addresses the ambiguity.

Based on input images, the system outputs a 3D reconstruction of the scene and estimates of the camera locations, which the robot would use to localize itself in the space.

“Once Dominic had the intuition to bridge these two worlds — learning-based approaches and traditional optimization methods — the implementation was fairly straightforward,” Carlone says. “Coming up with something this effective and simple has potential for a lot of applications.

Their system performed faster with less reconstruction error than other methods, without requiring special cameras or additional tools to process data. The researchers generated close-to-real-time 3D reconstructions of complex scenes like the inside of the MIT Chapel using only short videos captured on a cell phone.

The average error in these 3D reconstructions was less than 5 centimeters.

In the future, the researchers want to make their method more reliable for especially complicated scenes and work toward implementing it on real robots in challenging settings.

“Knowing about traditional geometry pays off. If you understand deeply what is going on in the model, you can get much better results and make things much more scalable,” Carlone says.

This work is supported, in part, by the U.S. National Science Foundation, U.S. Office of Naval Research, and the National Research Foundation of Korea. Carlone, currently on sabbatical as an Amazon Scholar, completed this work before he joined Amazon.

Claire chatted to Anthony Jules from Robust.AI about their autonomous warehouse robots that work alongside humans.

Anthony Jules is the CEO and co-founder of Robust.AI, a leader in AI-driven warehouse automation. The company’s flagship product Carter, is built to work with people in their existing environments, without disrupting their workflows. Anthony has a career spanning over 30 years at the intersection of robotics, AI, and business. An MIT-trained roboticist, he was part of the founding team at Sapient, held leadership roles at Activision, and has built multiple startups, bringing a unique blend of technical depth and operational scale to human-centered automation.

The dexterity gap: from human hand to robot hand

Observe your own hand. As you read this, it’s holding your phone or clicking your mouse with seemingly effortless grace. With over 20 degrees of freedom, human hands possess extraordinary dexterity, which can grip a heavy hammer, rotate a screwdriver, or instantly adjust when something slips.

With a similar structure to human hands, dexterous robot hands offer great potential:

Universal adaptability: Handling various objects from delicate needles to basketballs, adapting to each unique challenge in real time.

Fine manipulation: Executing complex tasks like key rotation, scissor use, and surgical procedures that are impossible with simple grippers.

Skill transfer: Their similarity to human hands makes them ideal for learning from vast human demonstration data.

Despite this potential, most current robots still rely on simple “grippers” due to the difficulties of dexterous manipulation. The pliers-like grippers are capable only of repetitive tasks in structured environments. This “dexterity gap” severely limits robots’ role in our daily lives.

Among all manipulation skills, grasping stands as the most fundamental. It is the gateway through which many other capabilities emerge. Without reliable grasping, robots cannot pick up tools, manipulate objects, or perform complex tasks. Therefore, we focus on equipping dexterous robots with the capability to robustly grasp diverse objects in this work.

The challenge: why dexterous grasping remains elusive

While humans can grasp almost any object with minimal conscious effort, the path to dexterous robotic grasping is fraught with fundamental challenges that have stymied researchers for decades:

High-dimensional control complexity. With 20+ degrees of freedom, dexterous hands present an astronomically large control space. Each finger’s movement affects the entire grasp, making it extremely difficult to determine optimal finger trajectories and force distributions in real-time. Which finger should move? How much force should be applied? How to adjust in real-time? These seemingly simple questions reveal the extraordinary complexity of dexterous grasping.

Generalization across diverse object shapes. Different objects demand fundamentally different grasp strategies. For example, spherical objects require enveloping grasps, while elongated objects need precision grips. The system must generalize across this vast diversity of shapes, sizes, and materials without explicit programming for each category.

Shape uncertainty under monocular vision. For practical deployment in daily life, robots must rely on single-camera systems—the most accessible and cost-effective sensing solution. Furthermore, we cannot assume prior knowledge of object meshes, CAD models, or detailed 3D information. This creates fundamental uncertainty: depth ambiguity, partial occlusions, and perspective distortions make it challenging to accurately perceive object geometry and plan appropriate grasps.

Our approach: RobustDexGrasp

To address these fundamental challenges, we present RobustDexGrasp, a novel framework that tackles each challenge with targeted solutions:

Teacher-student curriculum for high-dimensional control. We trained our system through a two-stage reinforcement learning process: first, a “teacher” policy learns ideal grasping strategies with privileged information (full object shape and tactile sensors) through extensive exploration in simulation. Then, a “student” policy learns from the teacher using only real-world perception (single-view point cloud, noisy joint positions) and adapts to real-world disturbances.

Hand-centric “intuition” for shape generalization. Instead of capturing complete 3D shape features, our method creates a simple “mental map” that only answers one question: “Where are the surfaces relative to my fingers right now?” This intuitive approach ignores irrelevant details (like color or decorative patterns) and focuses only on what matters for the grasp. It’s the difference between memorizing every detail of a chair versus just knowing where to put your hands to lift it—one is efficient and adaptable, the other is unnecessarily complicated.

Multi-modal perception for uncertainty reduction. Instead of relying on vision alone, we combine the camera’s view with the hand’s “body awareness” (proprioception—knowing where its joints are) and reconstructed “touch sensation” to cross-check and verify what it’s seeing. It’s like how you might squint at something unclear, then reach out to touch it to be sure. This multi-sense approach allows the robot to handle tricky objects that would confuse vision-only systems—grasping a transparent glass becomes possible because the hand “knows” it’s there, even when the camera struggles to see it clearly.

The results: from laboratory to reality

Trained on just 35 simulated objects, our system demonstrates excellent real-world capabilities:

Generalization: It achieved a 94.6% success rate across a diverse test set of 512 real-world objects, including challenging items like thin boxes, heavy tools, transparent bottles, and soft toys.

Robustness: The robot could maintain a secure grip even when a significant external force (equivalent to a 250g weight) was applied to the grasped object, showing far greater resilience than previous state-of-the-art methods.

Adaptation: When objects were accidentally bumped or slipped from its grasp, the policy dynamically adjusted finger positions and forces in real-time to recover, showcasing a level of closed-loop control previously difficult to achieve.

Beyond picking things up: enabling a new era of robotic manipulation

RobustDexGrasp represents a crucial step toward closing the dexterity gap between humans and robots. By enabling robots to grasp nearly any object with human-like reliability, we’re unlocking new possibilities for robotic applications beyond grasping itself. We demonstrated how it can be seamlessly integrated with other AI modules to perform complex, long-horizon manipulation tasks:

Grasping in clutter: Using an object segmentation model to identify the target object, our method enables the hand to pick a specific item from a crowded pile despite interference from other objects.

Task-oriented grasping: With a vision language model as the high-level planner and our method providing the low-level grasping skill, the robot hand can execute grasps for specific tasks, such as cleaning up the table or playing chess with a human.

Dynamic interaction: Using an object tracking module, our method can successfully control the robot hand to grasp objects moving on a conveyor belt.

Looking ahead, we aim to overcome current limitations, such as handling very small objects (which requires a smaller, more anthropomorphic hand) and performing non-prehensile interactions like pushing. The journey to true robotic dexterity is ongoing, and we are excited to be part of it.

Read the work in full

• RobustDexGrasp: Robust Dexterous Grasping of General Objects, Hui Zhang, Zijian Wu, Linyi Huang, Sammy Christen, Jie Song.

The Association for the Advancement of Artificial Intelligence (AAAI) is thrilled to announce the launch of a new podcast series, “Generations in Dialogue: Bridging Perspectives in AI.” Through this series, we aim to represent diverse perspectives across generations, fostering rich conversations about the ever changing landscape of AI. “Generations in Dialogue” aims to feature unique outlooks from AI experts, practitioners, enthusiasts of all backgrounds. Each episode will cover topics ranging from the history of AI, the latest research, to exploring the future ethical implications of its newest technologies. We will delve into how generational experiences shape views on AI, exploring the challenges, opportunities, and ethical considerations that come with the advancement of this transformative technology.

We welcome you to join us on this exciting journey. Whether you are an AI professional, researcher, or student, we hope this platform will be invaluable in discovering unique perspectives of AI with a global audience, bridging existing generational gaps in the understanding of AI and expanding your network beyond your local communities.

About the host

Ella Lan, a member of the AAAI Student Committee, is the host of “Generations in Dialogue: Bridging Perspectives in AI.” She is passionate about bringing together voices across career stages to explore the evolving landscape of artificial intelligence. Ella is a student at Stanford University tentatively studying Computer Science and Psychology, and she enjoys creating spaces where technical innovation intersects with ethical reflection, human values, and societal impact. Her interests span education, healthcare, and AI ethics, with a focus on building inclusive, interdisciplinary conversations that shape the future of responsible AI.

You can find out more about the series here, and you can also watch the first episodes on the AAAI YouTube channel.

Claire chatted to Heather Knight from Oregon State University about applying methods from the performing arts to robotics.

Heather Knight runs the CHARISMA Robotics research group. Her education includes a PhD on Expressive Motion for Low Degree of Freedom Robots from Carnegie Mellon University, and M.S. and B.S. degrees in EECS from the Massachusetts Institute of Technology. She has worked at NASA’s Jet Propulsion Laboratory and Aldebaran Robotics, and produced the Robot Film Festival, a Cyberflora robot flower garden, robot comedy on TED.com, and a two-floor Rube Goldberg machine for OK Go that won a British Video Music Award.

For food and beverage manufacturers, where production never stops, safety risks often hide in plain sight. One of the most common and costly examples is manual palletizing.

Repetitive lifting, twisting, and stacking at the end of the line lead to strain injuries, fatigue, and turnover. These tasks don’t just slow production; they quietly drain profit through absenteeism and compensation costs.

The good news: palletizing automation has evolved. With Robotiq’s PAL Ready and PAL Series, plant managers can reduce injuries, boost uptime, and create a safer, more productive workplace — starting today.

Why ergonomics and safety are strategic priorities

The ROI of ergonomics in manufacturing is surprisingly high. When workers avoid fatigue and injury, companies save on replacement labor, compensation claims, and downtime — while also improving morale and retention.

 Investing in robotic palletizing isn’t just about efficiency, it’s about protecting your workforce and your bottom line.

“[Manual palletizing] was an ergonomically unfriendly task. These cases are 30 pounds when they come off the line, at six cases per minute. We had two operators doing that job, and on a shift, they might lift a hundred thousand pounds. Those operators now have forklift certifications and have been reallocated to other areas of the plant where they were needed.

Mark Majkrzak
Founder at RAIN Pure Mountain Spring Water

If you'd like to take a deeper dive into this topic and evaluate the direct and indirect costs of Musculoskeletal Disorder (MSD) injuries,  I invite you to watch our exclusive webinar with safety expert, Carla Silver: Safety pays - making a case for a cobot palletizer to reduce ergonomic injuries and provide an ROI. 

PAL Ready: Safety from day one

PAL Ready is a production-ready palletizing cell — fully assembled, tested, and ready to run on day one. It’s built for real-world manufacturing environments where safety, reliability, and uptime matter most.

Key safety features include:

• Smart Infeed System: Automates box presentation, eliminating bending and twisting motions.
• Built-in Safety Scanners: Detect human presence and slow or pause robot motion instantly.
• Compact Footprint & Mobility: Fits tight spaces and moves easily between lines — no permanent fencing required.
• PowerPick Multi Vacuum Gripper: Reliable box handling and smooth motion control prevent unexpected collisions.
• Pre-validated Safety Logic: Every PAL Ready cell is tested before shipment to ensure compliance and peace of mind.

With PAL Ready, manufacturers can automate fast while ensuring a safe transition for their operators.

PAL Series: Scalable safety for expanding operations

When it’s time to scale, PAL Series takes safety and consistency to the next level. It’s a modular palletizing platform built on the same foundation as PAL Ready — but designed to grow with your factory.
Each deployment uses the same software, safety logic, operator interface, and design architecture, so scaling doesn’t mean retraining or revalidating from scratch.

Why it works:

• Standardized safety configuration across multiple sites
• Modular add-ons for growth without new integration work
• Consistent operator experience across every cell
• Scalable without the risk of custom one-offs

The true cost of manual palletizing

Manual palletizing tasks can involve 700+ lifts per worker per shift — repetitive motions that put enormous stress on the back, shoulders, and joints. Over time, this leads to lost time, worker turnover, and chronic injuries.

In contrast, collaborative palletizing systems like PAL Ready and PAL Series remove the ergonomic burden entirely. Cobots take on the heavy, repetitive work, while employees move into roles that require skill, oversight, and creative problem-solving.

This shift doesn’t eliminate jobs — it upgrades them. Workers stay safe, motivated, and productive, and companies see measurable gains in both safety and output.

October reminder: Safe work is smart work

During Safe Work Month, Robotiq celebrates the manufacturers who are transforming end-of-line operations into safer, smarter, and more sustainable workplaces.

When safety becomes the foundation of automation, everyone benefits:

• Operators go home healthy and motivated.
• Managers see predictable uptime and ROI.
• Factories become resilient and future-ready.
  
  

This October, take a fresh look at how your teams palletize. A small step toward automation today could prevent injuries — and drive long-term productivity — for years to come.

Ready to See Safe Automation in Action?

So, will Lean Palletizing bring safety and cost efficiency to your factory? Before committing, know exactly what automation can deliver for your production line(s).

???? Try the Robotiq Palletizing Fit Tool to receive:

• A custom 3D simulation of your palletizing cell.
• ROI and payback calculations based on your production data.
• A downloadable report you can share with your team.

Start fast. Scale with confidence.

That’s where automation earns its keep — but only if it can work safely alongside people.

The Hidden Cost of Manual Palletizing

A single repetitive motion might not seem like much, but multiplied over thousands of cycles a week, it becomes a serious risk.

• Musculoskeletal injuries from lifting and twisting
• Fatigue and strain from repetitive movement
• Accidents from overstacked pallets or unstable loads
  
  

From hazard to harmony: Designing PAL Ready for safe collaboration

PAL Ready was designed for the real world, one where humans and robots share the same workspace. Every detail supports safe, smooth interaction, without cages or complex barriers.

Here’s how it makes human-robot collaboration worry-free:

1. Built on collaborative robotics

PAL Ready uses a collaborative robot (cobot) designed to operate safely near people.

• Force and speed limits keep movement within safe parameters.
• Integrated safety sensors detect contact or abnormal resistance, stopping motion instantly.
• Rounded edges and lightweight design reduce impact risks.

It’s automation that works with your people.

2. Compact and contained

Safety starts with simplicity. PAL Ready has a compact footprint and keeps all components within a clearly defined zone. Operators can easily move around it without crossing into dangerous areas or tripping hazards.

The result is a clean, organized workspace that feels safe and stays safe.

3. Smart controls, predictable behavior

Every PAL Ready comes pre-programmed and pre-tested. That means no surprises in motion paths, speeds, or interactions.
Operators can confidently restart, pause, or adjust the system using an intuitive interface, with built-in safety stops and clear visual indicators.

No complicated coding. No unsafe improvisations.

4. Compliance that goes beyond checkboxes

PAL Ready meets international safety standards (ISO/TS 15066 for collaborative operation).

Paired with an on-site risk assessment, each system is fully assembled and ready to meet your specific safety standards, so you know it's safe from day one.

At Glenhaven Foods, operators were previously tasked with stacking heavy boxes by hand. After implementing Lean Palletizing, the company saw:

• Reduced ergonomic strain for all operators
• Consistent palletizing without downtime
• ROI in just 13 months, thanks to labor savings and higher throughput
  
  

“It’s a very quick, fast, simple solution, and it’s one that can be rolled out at pace across multiple lines within a factory.”
— Ken Maguire, Managing Director, Glenhaven Foods

Lean Palletizing allowed employees to focus on higher-value tasks while the cobot safely handled repetitive, physically demanding work.

A safer path to productivity

Automation isn’t just about output; it’s about keeping people safe while keeping production steady. PAL Ready replaces high-risk manual palletizing with a reliable cobot system, allowing operators to focus on higher-value, lower-risk work.

When safety and productivity align, everybody wins.

Visualize PAL Ready in your space

With the Robotiq Palletizing Fit Tool, you can:

✅ Validate your application in minutes
✅ Generate a custom 3D simulation of your palletizing cell
✅ Calculate ROI and payback period with your real data
✅ Get a downloadable report to share with your team

Start fast. Scale with confidence. 
Fill out the Palletizing Fit Tool today and see if Lean Palletizing is right for your operation.

Modern collaborative palletizing robots (also called cobots) make automation accessible, compact, and cost-effective for manufacturers of all sizes. Let’s separate fact from fiction.

Myth 1: “Robots and integration are too expensive.”

Reality: Automation costs have dropped dramatically.

Older systems required custom engineering, external integrators, and long commissioning phases. Robotiq PAL Ready changes that—it’s preassembled, pretested, and production-ready on day one.

For manufacturers needing flexibility, Robotiq PAL Series offers a modular platform that scales from a single line to multiple sites—without starting from scratch.

✅ Most factories achieve ROI in 1–2 years through labor savings, reduced downtime, and predictable performance.

"The ROI was a slam dunk from the beginning, from how much labor it saved. The installation process was the smoothest install of any piece of equipment I have had to experience in 28 years."
- Greg Thayer
Vice-President, Cascade Coffee

Automation isn’t a cost burden; it’s a short payback investment.

Myth 2: “Automation is only for mass production.”

Reality: Collaborative palletizing works for small and changing batches, too.

Robotiq Lean Palletizing software allows quick changeovers and easy reprogramming. Operators can adjust to new SKUs or box sizes in minutes—no advanced robotics experience required.

That means you can automate high-mix, low-volume production without sacrificing flexibility.

Myth 3: “We don’t have space for a robot cell.”

Reality: If you can fit a pallet, you can fit a PAL.

PAL Ready’s compact footprint takes up just about the size of half of a pallet. Built-in safety scanners replace traditional fencing, and its mobility features allow you to move it between lines or clear space in minutes.

Lean design means automation fits your factory, not the other way around.

Myth 4: “Automation replaces jobs.”

Reality: Robots take over repetitive, physically demanding work, not people’s creativity.

By automating palletizing, Robotiq PAL Ready and PAL Series improve workplace safety and morale. Operators are freed up to handle quality checks, process improvements, or more value-adding work.

We were able to automate a task that was extremely tedious. No one complains about back pain anymore. At least not from palletizing."
- Sebastian Raumland
Managing Director, Raumland GmbH

Automation enhances human work—it doesn’t eliminate it.

With Robotiq Lean Palletizing, you don’t need a big budget, a large team, or extra space to get started.
You just need a clear path—and that’s exactly what PAL Ready and PAL Series deliver.

Robotiq PAL Ready → Plug-and-play palletizing that’s production-ready on day one.
Robotiq PAL Series → Modular, scalable automation that grows with your business.

Want to know your exact ROI before you start?
???? Try the Robotiq Palletizing Fit Tool to get:

• A custom 3D simulation for your factory layout
• ROI and payback calculations
• A detailed report to share with your team

Start fast. Scale with confidence. 
Fill out the Palletizing Fit Tool today and see if Lean Palletizing is right for your operation.

That’s why Robotiq PAL Ready was engineered to fit right into real-world production environments—especially in the food and beverage industry, where space is limited and uptime matters.

Compact by design

PAL Ready takes up roughly the space of half a pallet. That’s the kind of footprint that allows you to add palletizing automation without reconfiguring your entire line.

Built-in safety scanners replace traditional fencing, maintaining an open, accessible layout that supports operator visibility and workflow efficiency. You get the benefits of automation—higher throughput, consistent stacking, and safer operations—without sacrificing space or accessibility.

Engineered for real production constraints

Most factories weren’t designed with robotics in mind. That’s why PAL Ready is preassembled, pretested, and production-ready on day one. You can place it where you’d normally fit a pallet, connect it to your line, and start palletizing right away.

Its modular and mobile design allows easy relocation between lines or seasonal setups. Whether you’re palletizing boxes or buckets, PAL Ready adapts to your environment instead of forcing you to adapt to it.

Real-world results: Panovo's space-saving success

When Panovo, a leading producer of snack and frozen bakery products, sought to automate palletizing, they faced one big challenge: limited floor space. Their existing facilities left little room for large machines or complex installations.

To solve this, Panovo partnered with Robotiq and HTL to deploy five Robotiq PAL Series cells—each handling different product formats and reaching up to 2,750 mm (108 in) in stack height.

The results speak for themselves:

• Consistent and standardized pallet formation, improving logistics efficiency

• Reliable operation even in low-temperature environments.

• A user-friendly interface that operators learned quickly—“as easy as using a smartphone.”

• Fast, supportive training from Robotiq and HTL’s technical teams.

By choosing a space-efficient collaborative solution, Panovo modernized their end-of-line process without expanding their footprint—proof that compact automation delivers large-scale impact.

"We had a big need to reduce operator fatigue in the packaging area, especially for palletizing. We required a low-cost, small-footprint solution because our space is limited and retrofitting big machines is difficult."

 

Juan Barba

Project Engineer, Panovo Alimentaria

Plan your fit in minutes

Wondering how it would look on your floor? The Robotiq Palletizing Fit Tool gives you:

• A custom 3D simulation of your factory layout

• ROI and payback calculations based on your operation

• A detailed report you can share with your team

With just a few clicks, you’ll know exactly how PAL Ready fits into your space AND your business case.

Lean automation that fits 

PAL Ready proves that automation doesn’t have to be big, complex, or disruptive.
It’s compact, collaborative, and ready to go—a lean palletizing solution that protects your people, boosts productivity, and fits your factory as-is.

Start fast. Scale with confidence. 

A standardized end-effector strategy lets you deliver faster, simplify maintenance, and scale your business without adding complexity.
It’s not about limiting flexibility, it’s about working smarter, not harder.

Discover how to standardize your gripper strategy and turn repeatability into your biggest advantage.

Why robotic integrators should standardize their gripper strategy

If you work as a robotic integrator, you understand how projects slow down fast with all those details. New robot brands come up. Unique part geometries appear. Specialized end effectors add more hassle.

A gripper change might seem straightforward in plans. It often turns into hours of extra engineering work. You deal with re-wiring. You handle troubleshooting too.

The best approach to regain predictability in your workflow involves standardizing your end-effector strategy. You use a common set of reliable grippers. They work as plug-and-play options across various robots. This cuts down integration time. It simplifies support efforts. It helps your business grow quicker.

What is end-effector standardization?

Your end effector serves as the robot's hand. The gripper interacts with the product in every cycle.

Projects demand new gripper types. They require adapter plates. They need I/O setups. Your team ends up recreating the same processes for each build.

Standardization means:

• Selecting a small, trusted set of grippers that can cover 80–90% of your use cases.
  
  
• Designing universal mechanical and electrical interfaces to reuse these tools.
  
  
• Building a repeatable process for setup, programming, and maintenance.
  
  

Standardization does not limit creativity. It reduces unnecessary friction. Your engineers focus on performance. They work on cycle times.

3 major benefits of a standardized gripper strategy

1. Save time and reduce integration costs

Every integrator has lost hours adapting a new gripper to a robot brand.
Different pinouts, mounting patterns, and communication protocols create unnecessary work.

By using a standard gripper platform like Robotiq’s adaptive electric grippers (2F-85, 2F-140, or Hand-E):

• You reuse your mechanical drawings and control templates across brands like Fanuc, Doosan, or Omron.
  
  
• Integration becomes faster and more predictable.
  
  
• Engineering hours shrink while project margins grow.

2. Simplify maintenance and field support

After a cell is running, custom grippers quickly become service headaches:
different spare parts, new programming, and longer downtime.

A standardized toolkit means:

• One set of spares to stock.
  
  
• Consistent service and documentation.
  
  
• Faster technician response times.
  
  

Technicians gain deep familiarity with fewer tools, while customers enjoy more reliable, easily supported systems.

3. Scale your integrations business efficiently

As your integrator business grows, you’ll handle more robot brands and industries.
Without a consistent approach, complexity grows faster than your team.

Standardization helps you:

• Reuse your design library and robot code templates.
  
  
• Train new engineers faster.
  
  
• Deliver more projects without increasing overhead.
  
  

Consistency becomes your competitive advantage.

Plug-and-play grippers: The key to easy standardization 

The biggest enabler of standardization is plug-and-play technology.
Modern grippers like the Robotiq 2F-85 or 2F-140 connect instantly with many popular collaborative and industrial robots.

They feature:

• Universal mounting systems
• Pre-configured communication drivers
• Intuitive setup interfaces
  
  

No need for custom coding. Configuration takes minutes, not hours — making multi-brand integration truly practical.

1. Audit your current tools — list the grippers and brands you use most.
   
   
2. Select a core toolkit — 2–3 models that cover most part types and sizes.
   
   
3. Document your templates — wiring diagrams, I/O configs, robot code.
   
   
4. Train your team — make standardization part of onboarding and SOPs.
   
   
5. Educate customers — explain that standardized tools reduce risk and lead time.
   
   

Start small. Pilot the approach on one or two projects, measure the impact, and expand from there.

FAQs about grippers and standardization

???? What’s the best robotic gripper for integrators?

Adaptive electric grippers are the most versatile. They can handle parts of different sizes and shapes, making them ideal for flexible, high-mix automation.

???? Can one gripper work across multiple robot brands?

Yes. Many plug-and-play grippers, like Robotiq’s, connect to Fanuc, Doosan, Omron, and other robots using ready-to-use mechanical kits and URCaps or similar software packages.

???? How does gripper standardization lower costs?

It reduces engineering hours, spare part diversity, and troubleshooting time. Integrators often save 15–30% per project through reuse and faster commissioning.

???? What’s the ROI on standardizing grippers?

Most integrators recover their initial effort within 2–3 projects, as repeatability cuts both design and service time.

???? Does standardization reduce flexibility?

No. Adaptive grippers are modular and configurable — you gain flexibility within a standardized platform instead of reinventing setups for each project.

The bottom line

Standardizing your end-effector and gripper strategy isn’t just about tools — it’s a way to make your integration business more efficient, predictable, and scalable.

By building around universal, plug-and-play grippers that work with all major robot brands, you eliminate repetitive engineering, improve uptime, and deliver a consistent customer experience.

That’s how top integrators turn technical complexity into a repeatable business advantage — one reliable gripper at a time.

At booth 1715, manufacturers discovered what it means to install a palletizing cell that runs on day one. PAL Ready arrived pre-assembled and production-ready, showing attendees that automation doesn’t have to be complex, costly, or slow to start.

PAL Ready’s compact design surprised everyone

One of the most common questions at the show?
“Will it fit on my floor?”

PAL Ready answered that question immediately.
With a footprint the size of half a pallet, it’s one of the most space-efficient palletizing solutions available today.

Visitors loved seeing how easily it can:

• Move between lines when production changes
• Clear space for cleaning or maintenance
• Operate safely alongside existing staff and equipment

For facilities with tight layouts or frequent washdowns, this level of flexibility makes automation practical, not just possible.

Safety was a top priority at EATS

Ergonomics and worker safety dominated conversations this year.
Manual palletizing still causes fatigue, repetitive strain, and injury risks for operators. PAL Ready showed how collaborative robots (cobots) can eliminate those risks while maintaining reliable throughput.

A great example came from Rain Pure Mountain Spring Water, a manufacturer that implemented Robotiq palletizing to reduce strain on workers and reassign them to higher-value tasks within the same facility.

Safety, productivity, and employee satisfaction—achieved in one step.

Key conversations from the show floor

Across the booths and sessions, a few consistent themes emerged among manufacturers:

1. Labor shortages are increasing the demand for cobot palletizing.
   Companies want automation that can start fast and scale easily. PAL Ready can be  up and running on day 1.
   
   
2. ROI expectations are shorter than ever.
   PAL Ready’s plug-and-play setup delivers measurable payback in 1-2 years.
   
   
3. Practical innovation wins over complexity.
   Manufacturers want automation that fits their existing processes.

PAL Ready’s mix of simplicity, speed, and adaptability fit perfectly with this new mindset.

A local highlight: the Chicago Bears giveaway ????

Of course, no Chicago event is complete without local pride.
Our own Mike Welch, a lifelong Bears fan, helped draw a crowd with our Chicago Bears jersey giveaway—and plenty of friendly competition among attendees.

What EATS showed us

The message was clear: manufacturers are ready for automation that’s
 ✅ Compact and flexible
 ✅ Safe for operators
 ✅ Fast to deploy
 ✅ Delivers quick ROI

PAL Ready proves that the next wave of automation doesn’t require more space or more complexity—just smarter design.

Couldn't make it to EATS?

Use our Palletizing Fit Tool to see how PAL Ready fits your factory floor and discover layout options customized to your space.

Because when it comes to palletizing, the right fit changes everything.

That’s why the shift toward standardized robotic grippers, particularly adaptive, plug-and-play grippers, is changing the way integrators work. Instead of starting from scratch, they’re building faster, more reliable, and more scalable automation cells.

Why standardization matters

Custom tooling used to be the default for integrators. Every project had a unique end effector, and each required its own mechanical design, wiring, and programming. It worked, but it wasn’t efficient.

A standard gripper eliminates all that repetition. It’s pre-engineered to fit multiple robot brands and comes ready to install out of the box. That means:

• No long design cycles
• No waiting on custom machining
• No complex setup or calibration

Standardization lets integrators reuse what works, saving time, reducing risk, and boosting profitability with every deployment.

What robotic integrators are asking

If you browse Reddit or professional forums like Robot Forum or Automation Stack Exchange, one question comes up again and again:

“How do I choose the right standard gripper for different parts, weights, and materials?”

It’s a good question. For integrators, the ideal gripper needs to handle multiple SKUs, adapt to different shapes, and maintain precision and reliability, all while being easy to program.

That’s where Robotiq’s adaptive grippers shine.

Why adaptive grippers are the smart choice

Unlike traditional pneumatic or custom grippers, adaptive grippers use electric actuation and smart control to automatically adjust to different part geometries.

With Robotiq’s line of adaptive grippers, including the 2F-85, 2F-140, and Hand-E, Hand-E C10 integrators can handle a wide range of parts without changing tooling.

These grippers are:

• Plug-and-play compatible with leading cobot brands such as Fanuc, Doosan, Omron, and Universal Robots
  
  
• Fully electric, requiring no external air supply
  
  
• Easily programmable through intuitive interfaces
  
  
• Reliable and field-proven, with thousands of installations worldwide
  
  

Whether you’re automating palletizing, machine tending, or packaging, Robotiq’s standard grippers help integrators deliver faster, simpler, and more flexible solutions.

Five factors to consider when choosing a standard gripper

1. Compatibility

Choose a gripper that works across multiple robot brands. Robotiq’s adaptive models integrate seamlessly with Fanuc, Doosan, Omron, and Universal Robots, reducing the number of tools your team needs to master.

2. Flexibility

The best standard grippers can handle a variety of part sizes, materials, and shapes without changing tooling. Adaptive fingers and adjustable force control make this possible.

3. East of use

Integration should be fast. Look for grippers that offer plug-and-play setup, preconfigured software, and simple reprogramming for new tasks.

4. Reliability

A gripper should be built to last meaning consistent performance over thousands of cycles so fewer service calls and happier customers.

5. Support and ecosystem

Standardization only pays off when you can count on long-term support. Robotiq offers a robust ecosystem with documentation, updates, and global support, giving integrators confidence on every project.

How standard grippers improve integrator ROI

Every custom end effector adds engineering time, complexity, and risk. By using a standardized adaptive gripper, you get:

• Up to 30% faster deployment time
  
  
• Reduced maintenance and spare parts
  
  
• Easier scalability across customers and applications
  
  

For integrators, this means more billable projects per year and fewer late-night troubleshooting sessions.

A real-world example: One gripper, many tasks

One U.S.-based integrator recently shared how they used a Robotiq 2F-140 gripper for both palletizing and case handling, without any hardware changes.

By switching only the robot program, the same gripper handled different box sizes, weights, and materials.

That kind of versatility used to require custom tooling for each job. Now, it’s standard.

The industry is moving fast. Manufacturers want flexible automation, quick deployments, and predictable results. Integrators who adopt standard, adaptive grippers are already ahead of the curve.

They spend less time engineering and more time delivering value.

Ready to simplify your next integration?

You don’t need to reinvent your gripper setup for every new customer.
Discover how Robotiq’s adaptive, plug-and-play grippers can help you deliver faster, safer, and more profitable automation projects.

???? Buy your Robotiq gripper directly today

Fast, flexible, and factory-friendly

Lean Palletizing includes two paths: PAL Ready and PAL Series. Manufacturers can choose the one that suits their factory environment best.

The PAL Ready palletizer from Robotiq is a pre-assembled, production-ready robotic palletizing cell designed for manufacturers who want fast automation without complexity. 

PAL Series delivers modular palletizing through configurable standard models and modules. Manufacturers can start small and scale to multiple cells or multi-site deployments

Both are built on Lean Robotics principles and deliver high throughput, compact design, and plug-and-play deployment—making it ideal for high-mix, low-volume operations or plants with limited floor space.

Key Specs of Robotiq Lean Palletizing at a Glance

• Max Payload on PAL Series: 32 kg (70 lb)
• Throughput: Up to 13 cycles per minute
• Stacking Height on PAL Series: 3000 mm (118 in)
• Footprint: Half a pallet

Plug-and-play automation

Unlike conventional palletizing systems that take weeks to configure, PAL Ready arrives pre-assembled. It can be installed and redeployed quickly, requiring no anchoring, forklifts, or engineering intervention.

This anchorless setup makes it ideal for:

• Multi-line manufacturing with frequent changeovers
• Washdown zones in food & beverage
• Seasonal or contract packaging lines
  
  

PAL Ready includes integrated safeguarding for operator safety and Robotiq’s Material Handling Copilot Software, an intuitive, no-code interface that allows any operator to launch or adjust recipes in minutes.

Engineered for performance and uptime 

PAL Ready’s smart infeed and flexible vacuum gripper adapt automatically to different SKUs, boxes, and pallet patterns—no manual tool changes required.

Key features include:

• Continuous operation logic: Keeps production running through brief stoppages
• Automatic box alignment: Ensures precise placement every cycle
• Unlimited SKU compatibility: Handle mixed-product runs easily
  
  

Together, these features maximize throughput and minimize downtime, ensuring consistent pallet quality across shifts.

PAL Ready stands out among cobot palletizers because it blends industrial performance with ease of use. It embodies modern end-of-line automation: scalable, adaptable, and human-friendly.

Customer success and global support

Real-world users praise Lean Palletizing’s speed and ease of use.

“It’s a very quick, fast, simple solution—and it can be rolled out across multiple lines within a factory.”

 

Ken Maguire

Managing Director, Glenhaven

With Robotiq’s Global Partner Network, customers gain local integration, installation, and support—ensuring every deployment runs smoothly and delivers rapid ROI.

The smart way to automate palletizing

Lean Palletizing gives manufacturers a fast, reliable way to automate palletizing without disrupting production.

Whether you’re running food & beverage, logistics, or consumer goods operations, PAL Ready and PAL Series are the compact cobot palletizers that help you increase throughput, improve safety, and maximize ROI.

Find your fit with the  Robotiq Palletizing Fit Tool. You'll get:

• A custom 3D simulation of your factory layout

• ROI and payback calculations based on your operation

• A detailed report you can share with your team

Lean automation that fits 

PAL Ready proves that automation doesn’t have to be big, complex, or disruptive.
It’s compact, collaborative, and ready to go—a lean palletizing solution that protects your people, boosts productivity, and fits your factory as-is.

Start fast. Scale with confidence. 

At Griffith Foods Colombia, operators were lifting, twisting, and stacking thousands of boxes every day on two high-volume doypack lines. Ergonomic assessments confirmed what workers already felt: the repetitive palletizing work carried a high risk of musculoskeletal injuries.

But instead of choosing between safety and productivity, Griffith Foods found a way to achieve both.

From 10,000 repetitive motions to zero

Each day, operators on Line 1 made 5,775 repetitive movements, and those on Line 2 made 5,005 — all now eliminated with the installation of a Robotiq PE10 Lean Palletizer.

"Today those movements went to zero thanks to the robot. People finish less tired "
- Santiago Ospina
Plant Manager at Griffith Foods Colombia

With support from IGPS, Robotiq’s integration partner, Griffith Foods deployed Colombia’s first Robotiq Lean Palletizing cell — designed to boost safety, maintain human-centered operations, and support high-volume production.

Built for flexibility, engineered for reliability 

Griffith Foods’ operation is anything but simple. The plant handles 32 SKUs across multiple box formats and pallet patterns—feeding into a single palletizing cell. The PE10 manages it all with precision: operating at 10 cycles per minute, handling loads up to 12.5 kg (28 lb), stacking up to a height of over 2,750 mm (108 in), and managing multiple SKUs from two production lines. 

The integrator used a digital twin model during design and engaged in comprehensive Factory Acceptance Testing (FAT) to ensure the system would run smoothly from day one.

Since its installation in August 2024, the system has achieved zero intrinsic equipment failures.

“Whenever we’ve had stoppages, they’ve been due to external factors, not the robot.”
- Santiago Ospina

Human-centered automation in action

For Griffith Foods, automation is about protecting humans—not replacing them. The new palletizing cell removed the heaviest, most repetitive tasks, enabling staff to be redeployed into higher-value roles while operating with improved well-being and productivity.

Safety features were built in from the start: proximity sensors slow the cobot’s motion when an operator enters the risk zone; collision contact would be soft and non-injurious. 
ProFood World

Resource-allocation also improved:

“We went from needing six people on the line to only four… Our aim was to free up personnel for deployment in higher-need areas.”

The difference was not just in head-count but in work-life balance. One shift was eliminated; staff gained more rest and family time. 

The project aligned with Griffith Foods’ internal Lean framework (GPS – Griffith Production System), and the installed system was recognized internally under the “Orange Bell” program, earning second place globally within the company. 

The takeaway

When a company with a people-first culture embraces automation, it sets a powerful example for the industry. At Griffith Foods Colombia, human-centred automation isn’t a buzzword—it’s how they’ve eliminated 10,000 repetitive motions per day, improved ergonomics, and achieved consistent uptime with no equipment failures.

Because when technology works for people, productivity follows naturally.

Want more stories from real factories like yours?

Follow Robotiq on LinkedIn and join over 75,000 manufacturers seeing how automation keeps people safe, and production running strong.

With new features like Pick Automatic, enhanced Multi-Pick, and upcoming Pick Optimization, Robotiq is pushing palletizing software further toward what factories really need: fast deployment, flexible operation, and zero headaches.

1. Pick Automatic: Forget teaching every pick 

Manual teaching for every new SKU? Not anymore.

With Pick Automatic, you no longer need to teach each pick position for new products. Once the infeed is calibrated — a step handled by Robotiq staff, distributors, or integrators before deployment — you’re good to go.

From there, operators simply:

• Create a new recipe
• Enter product dimensions and weight
• Select the infeed
  
  

That’s it. The software does the rest — automatically calculating the robot’s pick pose so you can focus on the end result: a neatly palletized, production-ready stack.

Best of all, Pick Automatic is available as a free software update for all existing Robotiq palletizer customers (except the ones using Universal Robots CB series) using a fixed-corner conveyor like the Smart Infeed.

It’s a small step for setup — and a giant leap for usability.

2. Multi-pick: Now faster, smarter, and easier to pick up

Dual or triple pick has never been this easy.

The improved Multi-Pick functionality makes handling multiple boxes per cycle straightforward — without needing to define a “grouped” box or modify dimensions manually. You simply choose between single, dual, or triple pick directly in the URCap interface.

The system then automatically adjusts:

• Box dimensions
• Pick positions
• Counting, gate management and box sensor configuration (when using Smart Infeed)
  
  

In other words, Multi-Pick just works — giving you the flexibility to boost throughput without extra programming or manual setup.

3. Coming soon: Pick optimization with PowerPick Multi  

For users running the PowerPick Multi gripper, another layer of intelligence is on the way.

The upcoming Pick Optimization feature automatically determines the best pick offset and position for each box, removing the need for manual tuning. The system analyzes several factors in real time, including:

• Maintaining at least six sealed cups per active zone
• Maximizing suction efficiency
• Avoiding suction overlap at box junctions
• Preventing unwanted suction on boxes still waiting on the conveyor
  
  

More reliable handling, fewer adjustments, and consistent performance even with changing box configurations.

This feature will be available soon — and, like every Robotiq update, it’s designed to make automation simpler, not harder.

Why it matters

Software updates may sound like small wins — but on a busy factory floor, they can mean hours saved, smoother transitions between SKUs, and a faster return on investment.

Each of these new tools brings Robotiq’s mission closer to reality: automation that’s easy to deploy, effortless to run, and built around the people who use it.

1. Home Automation and Smart Living

Robots are making our homes smarter and our lives easier. Robotic vacuum cleaners, like the Roomba, can clean our floors by themselves. Smart home assistants, like Amazon Alexa and Google Home, help us manage our schedules, control smart devices, and get information just by asking. These robots make our daily tasks much simpler and more organized.

2. Healthcare and Medical Robotics

In healthcare, robots are helping doctors and patients in amazing ways. Surgical robots, like the da Vinci Surgical System, allow doctors to perform surgeries with great precision. This means fewer complications and faster recovery times for patients. Robotic exoskeletons help people with physical disabilities move around more easily. Robots in hospitals can deliver medicines, monitor patients, and even help with cleaning, making healthcare safer and more efficient.

3. Industrial Automation and Manufacturing

In factories, robots are essential for making production faster and more efficient. They can perform repetitive tasks like assembling, welding, and painting with great accuracy and speed. This not only reduces costs but also improves product quality. Collaborative robots, or cobots, work alongside humans, making workplaces safer and more productive.

4. Education and Learning

Robots are changing the way students learn in classrooms. Educational robots, like LEGO Mindstorms and NAO robots, make learning fun and interactive. They teach programming, problem-solving, and critical thinking skills. Robots also support special education, helping children with learning disabilities improve their social and communication skills.

5. Service and Hospitality Industry

The service and hospitality industry is using robots to improve customer experiences and efficiency. Robots in hotels help with check-ins, luggage handling, and room service. In restaurants, robotic chefs and waitstaff prepare and serve food, ensuring consistency and hygiene. Retail stores use service robots for inventory management and assisting customers, making shopping more enjoyable.

6. Transportation and Logistics

Autonomous vehicles and drones are changing transportation and logistics. Self-driving cars are being tested for ride-sharing services and personal transportation, promising safer travel. Delivery drones transport packages quickly, especially in remote areas. Robots in warehouses handle tasks like sorting, packing, and transporting goods, making logistics more efficient.

Challenges and Future Prospects

While robots bring many benefits, there are also challenges to address, such as job displacement and data privacy. It’s important to make sure that robots complement human work instead of replacing it, creating new job opportunities and skill development.

The future of robotics is bright. As technology continues to improve, robots will become even smarter and more capable. They will help solve global challenges, from healthcare to environmental sustainability. By working together with robots, we can create a future where both humans and robots thrive.

In conclusion, robots are changing our world, making it more efficient, convenient, and innovative. Embracing this robotic revolution means navigating challenges and opportunities, creating a future where humans and robots work together. The possibilities are endless, and the journey has just begun.

Written/published by Kevin Marshall with the help of AI models (AI Quantum Intelligence)

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