At InDro Robotics, we sell a lot of robots and drones for the purpose of research and development.Those devices range all the way from small and highly affordable out-of-the-box solutions like the <a href="https://store.indrorobotics.com/products/limo-pro-robot" target="_blank">LIMO PRO</a> right through to highly complex builds for some of the largest technology companies in the world. And that image above? A recent build with a manipulator arm (and many other capabilities) for a client.But what makes for an effective R&amp;D robot? We put that question to Luke Corbeth, Head of R&amp;D sales. Broadly speaking, he identifies four pillars of research when it comes to R&amp;D. They are:<ul><li>Control</li><li>Planning</li><li>Perception</li><li>Interaction</li></ul>In fact, Corbeth recently pulled together a graphic explaining these pillars:<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743190032007-Research-Pillars-LI-1.png.webp" style="width:100%px" class="fr-fic fr-dib" alt="Mobile Robot Research Pillars" /><h3 id="isPasted">Crossover and Customization</h3>While the four pillars are all distinct, they’re not mutually exclusive. R&amp;D might include both planning and perception, or any other combination of the above. And the focus of the R&amp;D will obviously inform what sensors – even what locomotion – are ultimately required. Does the robot need autonomy, or will the client be coding their own autonomy stack? Is Simultaneous Localisation And Mapping (SLAM) required? Does the robot need to be able to navigate stairs? For our clients, these questions are all discussed in great detail during an initial discovery call with Corbeth.Sometimes, particularly in lab-based work, an out-of-the-box solution may be all that’s necessary. At <a href="https://www.bu.edu/" target="_blank">Boston University</a>, for example, they have a fleet of LIMOs deployed in the lab for <a href="https://indrorobotics.ca/boston-university-uses-agilex-limo-for-research/" target="_blank">research on multi-agent systems</a> (and other areas). R&amp;D in the field, by contrast, generally calls for a larger and more robust type of robot.“A project in the lab often means you can get away with a smaller platform,” says Corbeth. “And when people are trying to tackle problems in the real world they’re often using larger platforms.”While this is generally true, there’s no question some indoor R&amp;D can require incredibly sophisticated robots. One of our more complex builds, which we affectionately named Rosie, is a dual-manipulator robot designed for the Industry 4.0 setting. In fact, the entire lab itself is built for <a href="https://indrorobotics.ca/industry-4-0-and-indro-the-evolution-continues/" target="_blank">4IR</a> – with a suite of interconnected devices that share data not only within that location itself, but also with other R&amp;D labs. (It’s actually a super interesting project, <a href="https://indrorobotics.ca/dual-manipulator-rosie-the-robot-used-for-industry-4-0-research/" target="_blank">which we explored in detail here</a>.)Regardless, the planned R&amp;D will inform what’s needed – including platform, sensors, etc. That’s where InDro has extensive expertise, not only in integration but in having tested and proven the components themselves. And that saves clients a lot of time and energy.“If every client or research lab had to build a robot from scratch, it would take them a lot longer to get to the point where they need to be,” explains Corbeth. “And that’s largely why we come into the picture. We help jumpstart these projects and get them to their end goal much faster.”Because InDro has years of experience building both custom robots for clients and our own products, we’ve learned – sometimes painfully – which components and platforms offer the best value and reliability. And, in conjunction with <a href="https://indrorobotics.ca/from-napkin-sketch-to-prototype-reality-indro-forge-does-it-all/" target="_blank">InDro Forge</a>, we have the expertise for seamless integration,“We’ve tested a wide range of different hardware and configurations,” he says. “We’ve basically swallowed that pill already so that our clients don’t have to.”Plus, of course, if a customer already has some components they want to use, such as a pricey LiDAR, we can customise a package to exclude that and save the end-user money.Below: Rosie, a dual-manipulator mobile robot we built for pick-and-place in a lab doing Industry 4.0 research<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743190087854-image-7.png.webp" style="width:100%px" class="fr-fic fr-dib" alt="Rosie Dual Manipulator Robot" /> <h3 id="isPasted">Plus, Of Course, Open Source</h3>Everything we sell, when it comes to robots, is Open Source and nearly always with ROS 2 (Robot Operating System 2). This is a significant upgrade from ROS 1, which relied on a Master-Slave architecture. By making that architecture more distributed (eliminating that central ROS Master), ROS 2 reduces single points of failure and is more scalable. It’s also what those in the R&amp;D space generally want, as Open Source allows them to easily pull in pre-existing code suitable to their research.“The main thing with Open Source is not having to start at zero. If everything was Closed Source, you’d have to do everything from scratch,” says Corbeth. “That’s the value of Open Source; you’re building off of the discoveries of your peers, and that dramatically expedites progress for everyone who is Open-Sourcing their projects.”And a final thing worth mentioning? Support. InDro has built a solid reputation for its after-sale support. From warranties and remote troubleshooting through to site visits (on the rare occasions that becomes necessary), we back what we build. We believe in minimising downtime for our clients so they can get on with R&amp;D.And remember those four R&amp;D pillars? It’s a great top-level view. But Corbeth also took the time to drill down within those themes to take a far more detailed look at research areas and use-cases. It’s amazing the number of areas where research is taking place (and there are likely even more that could be added):<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743190141006-Robotics-Research-Themes-LI-1.png.webp" style="width:100%px" class="fr-fic fr-dib" alt="Robotics research themes" /><h3 id="isPasted">InDro's Take</h3>We’re in kind of a unique position when it comes to helping clients requiring robots or drones for Research and Development – because we’re an R&amp;D company ourselves. In addition to finding the best solution for customers (whether it’s out-of-the-box or a complex custom build), we are continuously developing our own products. Some of those products, like <a href="https://indrorobotics.ca/indro-commander-module-streamlines-robotics-rd/" target="_blank">InDro Commander</a> and the forthcoming <a href="https://indrorobotics.ca/the-new-indro-command-module-amazing-power-in-a-tiny-package/" target="_blank">InDro Cortex</a> have been designed for those clients who want to build or modify their own robots with ease. We truly understand the R&amp;D journey – and have something of a special affinity for clients in that space.“From the very outset, InDro Robotics was formed as a Research and Development company, so we truly get it,” says Founder and CEO Philip Reece. “It’s in the interests of the entire robotics industry to see advances in the R&amp;D space – so we’re always happy to assist with solutions from the simple to the complex. It really is what we do.”Want to continue the conversation? Feel free to contact us <a href="https://indrorobotics.ca/connectwithanexpert/" target="_blank">here</a>. He’s always happy to talk robotics with zero pressure.

Robots and drones for the purpose of research and development.

What Makes an Effective Research and Development Robot?

As a doctoral student at ETH, Moritz Geilinger was immersed in theoretical considerations about the planning and controlling of robot movements when a very practical request from Swiss Post landed on his desk. The question was: which tasks in the package handling process can be automated?Together with his colleague Simon Huber, who was writing his doctoral thesis next door, Geilinger examined Swiss Post’s sorting systems. The two researchers were astonished to find that although the packages were automatically sorted on the conveyor belts, they were unloaded and reloaded onto the lorries by hand. This is partly due to the initial costs, says Geilinger: “Automation systems are set up individually for specific tasks, which takes a lot of time and resources.” A more flexible system was therefore needed in order to achieve additional automation.<h2>Making robots smarter</h2>That is precisely the work of the researchers in Professor Stelian Coros’s Computational Robotics Lab – namely, making robots smarter and more adaptive. The group was founded in 2017 with the two future young entrepreneurs as its first doctoral students. “The special thing about the lab is that we all came to robotics from computer science,” says Geilinger.He himself developed what’s known as a differentiable physics simulation. “This allows us to predict what happens after a specific action, such as how the position of objects changes when the robot approaches them,” he explains. In this context, differentiable means that the simulation shows what would happen if the position of another object were moved. Huber, for his part, worked on a technique known as differentiable collision avoidance.Armed with this knowledge, the two researchers jointly developed an algorithm that allows robots to pack objects of different sizes and shapes as tightly as possible together, allowing them to be transported cost-effectively from one place to another.<iframe width="640" height="360" src="https://www.youtube.com/embed/mFihv9M3pL0?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe><h2 id="isPasted">Enabling robots to work together</h2>What makes this solution so unique? “We can get a robot ready for defined tasks within minutes,” says Geilinger. According to the researcher, the software also allows the robots to work in coordination with one another, for example to lift a package between three of them if necessary. This removes the need to set up expensive new infrastructure for new tasks. Instead of larger and heavier robots, several smaller ones are used instead.“The request from Swiss Post was a real stroke of luck for us,” says Geilinger. This practical task served as the trigger for founding Flink Robotics as a spin-off in 2023. Geilinger assumed the role of CEO, and his colleague Simon Huber that of CTO. Their professor, Stelian Coros, was the third person to join the company, in the capacity of an adviser.A year later, the young entrepreneurs had already signed up six employees and received the Venture Kick Award, which comes with prize money of CHF 150,000. What was the secret to their successful launch?<h2>A team of bright minds</h2>“It might sound a bit overconfident, but we’re a team of bright minds whose proximity to research means we deal with state-of-the-art technology and can solve practically any problem in the field of robotics,” says Geilinger. “At the same time, it’s important for us to be very close to the problem, or rather to the end customer, in order to find out why certain tasks haven’t been automated yet.” <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1743085152879-image.imageformat.1286.1867601149.jpg" style="width:100%px" class="fr-fic fr-dib" />“Thanks to our proximity to research, our team can solve practically any problem in the field of robotics,” says Flink CEO Moritz Geilinger (back row, third from right).  (Image: Nicole Davidson / ETH Zurich)Flink Robotics is therefore neither a technology company that is building an all-capable robot nor a system integrator that is simply solving a specific problem. “We try to focus on a concrete application for which there is high demand, and then refine the general technology while resolving the use case,” Geilinger explains.<h2>Countless potential customers</h2>Regarding the example of loading packages at a distribution hub, he says: “We’re currently seeing a great deal of interest – not only from Swiss Post but also from Österreichische Post and DHL.” As soon as the technical problem is resolved, he adds, there will be an extremely scalable business model to hand – particularly with a view to the 13 variously sized Swiss Post sorting offices and the over 30 DHL distribution centres in Germany.Another interesting area of business is the final packaging of products. “Ultimately, products are often still packed into cardboard boxes and loaded onto pallets by hand,” says Geilinger. The young entrepreneurs have already visited chocolate manufacturers and producers of face creams that manufacture hundreds of different products, operating perhaps six production lines in a single factory. With classical robots, this would require repeated reprogramming and adaptation to each product.<h2>Managing company growth</h2>In other words, the young entrepreneurs have no shortage of potential customers. The big challenge for them at present is to juggle all of these different tasks. They are continuing to develop their algorithms for existing customers and to carry out test installations on site.  At the same time, new employees need to be introduced. “Time and time again, we have to turn customers away because we don’t have the capacity to initiate further developments,” says Geilinger.So far, Flink Robotics has been very fortunate with its staff given that computer science and robotics graduates are also particularly sought-after by global companies in Zurich. “We attract very specific personalities who feel comfortable in a small, agile team, who like to build things themselves and who feel it’s important for their own work to have a directly visible impact,” says the young CEO.At Flink Robotics, the staff don’t just carry out simulations in the office. “Every few weeks, we load the robots into the car and drive to a distribution hub, where we see what we’ve actually programmed and how much better the robot has become,” says Geilinger enthusiastically.It is there, on site at the customers’ premises, that the long-term success of Flink Robotics will be decided. “The big challenge for venture companies in robotics is to actually put a new technology into practice rather than just demonstrating it in the office,” says Geilinger.<a></a><h2>Looking ahead with confidence</h2>The researcher is confident about the future: “The objective we’ve set ourselves is for our software to be in use in production in a year’s time.” That will probably involve growing the team further. “Carefully, as before,” says Geilinger. At the same time, Flink Robotics also wants to expand to other business areas, such as those relating to final packaging.The aim is for Flink Robotics to be a well-known name in the package handling industry and other relevant industries across Europe within three years. “Flink Robotics will stand for a new breed of automation that is less fixed, less preprogrammed, and much more adaptive and flexible.”

Moritz Geilinger (left) and Simon Huber, the founders of Flink Robotics in their laboratory.  (Image: Nicole Davidson / ETH Zurich)

The ETH spin-off Flink Robotics wants to revolutionize the handling of packages. Its founders Moritz Geilinger and Simon Huber have developed software that allows robots to work together and quickly take on new tasks.

Readying robots for new tasks

Prototyping large structures with integrated electronics, like a chair that can monitor someone’s sitting posture, is typically a laborious and wasteful process.One might need to fabricate multiple versions of the chair structure via 3D printing and laser cutting, generating a great deal of waste, before assembling the frame, grafting sensors and other fragile electronics onto it, and then wiring it up to create a working device.If the prototype fails, the maker will likely have no choice but to discard it and go back to the drawing board.MIT researchers have come up with a better way to iteratively design large and sturdy interactive structures. They developed a rapid development platform that utilizes reconfigurable building blocks with integrated electronics that can be assembled into complex, functional devices. Rather than building electronics into a structure, the electronics become the structure.These lightweight three-dimensional lattice building blocks, known as voxels, have high strength and stiffness, along with integrated sensing, response, and processing abilities that enable users without mechanical or electrical engineering expertise to rapidly produce interactive electronic devices.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742995091318-MIT-Voxel-Kit-02-press.jpg" style="width:100%px" class="fr-fic fr-dib" />The lightweight three-dimensional lattice building blocks, known as voxels, have high strength and stiffness, along with integrated sensing, response, and processing abilities. Photo: Jack FormanThe voxels, which can be assembled, disassembled, and reconfigured almost infinitely into various forms, cost about 50 cents each.The prototyping platform, called VIK (Voxel Invention Kit), includes a user-friendly design tool that enables end-to-end prototyping, allowing a user to simulate the structure’s response to mechanical loads and iterate on the design as needed.“This is about democratizing access to functional interactive devices. With VIK, there is no 3D printing or laser cutting required. If you just have the voxel faces, you are able to produce these interactive structures anywhere you want,” says Jack Forman, an MIT graduate student in media arts and sciences and affiliate of the MIT Center for Bits and Atoms (CBA) and the MIT Media Lab, and co-lead author of a <a href="https://cba.mit.edu/docs/papers/25.05.VIK-CHI.pdf" target="_blank">paper on VIK</a>.Forman is joined on the paper by co-lead author and fellow graduate student Miana Smith; graduate student Amira Abdel-Rahman; and senior author Neil Gershenfeld, an MIT professor and director of the CBA. The research will be presented at the Conference on Human Factors in Computing Systems.<h2>Functional building blocks</h2>VIK builds upon years of work in the CBA to develop discrete, cellular building blocks called voxels. One voxel, an aluminum cuboctahedra lattice (which has eight triangular faces and six square faces), is strong enough to support 228 kilograms, or about the weight of an upright piano.Instead of being 3D printed, milled, or laser cut, voxels are assembled into largescale, strong, durable structures like <a href="https://news.mit.edu/2020/versatile-building-blocks-1118" target="_blank">airplane components or wind turbines</a> that can respond to their environments.The CBA team merged voxels other work in their lab centered on interconnected electrical components, yielding <a href="https://cba.mit.edu/docs/papers/23.11.voxelcopter.pdf" target="_blank">voxels with structural electronics</a>. Assembling these functional voxels generates structures that can transmit data and power, as well as mechanical forces, without the need for wires.They used these electromechanical building blocks to develop VIK.“It was an interesting challenge to think about adapting a lot of our previous work, which has been about hitting hard engineering metrics, into a user-friendly system that makes sense and is fun and easy for people to work with,” Smith says.For instance, they made the voxel design larger so the lattice structures are easier for human hands to assemble and disassemble. They also added aluminum cross-bracing to the units to improve their strength and stability.In addition, VIK voxels have a reversible, snap-fit connection so a user can seamlessly assemble them without the need for additional tools, in contrast to some prior voxel designs that used rivets as fasteners.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742995119644-MIT-Voxel-Kit-02a-press.jpg" style="width:100%px" class="fr-fic fr-dib" />Assembling these functional voxels generates structures that can transmit data and power, as well as mechanical forces, without the need for wires. Photo: Jack Forman“We designed the voxel faces to permit only the correct connections. That means that, if you are building with voxels, you are guaranteed to be building the correct wiring harness. Once you finish your device, you can just plug it in and it will work,” says Smith.Wiring harnesses can add significant cost to functional systems and can often be a source of failure.<h2>An accessible prototyping platform</h2>To help users who have minimal engineering expertise create a wide array of interactive devices, the team developed a user-friendly interface to simulate 3D voxel structures.The interface includes a Finite Element Analysis (FEA) simulation model that enables users to draw out a structure and simulate the forces and mechanical loads that will be applied to it. It adds colors to an animation of the user’s device to identify potential points of failure.“We created what is essentially a ‘Minecraft’ for voxel applications. You don’t need a good sense of civil engineering or truss analysis to verify that the structure you are making is safe. Anyone can build something with VIK and have confidence in it,” Forman says.Users can also easily integrate off-the-shelf modules, like speakers, sensors, or actuators, into their device. VIK emphasizes flexibility, enabling makers to use the types of microcontrollers they are comfortable with.“The next evolution of electronics will be in three-dimensional space and the Voxel Invention Kit (VIK) is the stepping stone that will enable users, designers, and innovators a way to visualize and integrate electronics directly into structures,” says Victor Zaderej, manager of advanced electronics packaging technology at Molex, a manufacturer of electronic, electrical, and fiber optic connectivity systems. “Think of the VIK as the merging of a LEGO building kit and an electronics breadboard. When creative engineers and designers begin thinking about potential applications, the opportunities and unique products that will be enabled will be limitless.”Using the design tool for feedback, a maker can rapidly change the configuration of voxels to adjust a prototype or disassemble the structure to build something new. If the user eventually wishes to discard the device, the aluminum voxels are fully recyclable.This reconfigurability and recyclability, along with the high strength, high stiffness, light weight, and integrated electronics of the voxels, could make VIK especially well-suited for applications such as theatrical stage design, where stage managers want to support actors safely with customizable set pieces that might only exist for a few days.And by enabling the rapid-prototyping of large, complex structures, VIK could also have future applications in areas like space fabrication or in the development of smart buildings and intelligent infrastructure for sustainable cities.But for the researchers, perhaps the most important next step will be to get VIK out into the world to see what users come up with.“These voxels are now so readily available that someone can use them in their day-to-day life. It will be exciting to see what they can do and create with VIK,” adds Forman.

A new rapid prototyping platform, VIK, utilizes reconfigurable building blocks with integrated electronics that can be assembled into complex, functional devices. Photo: Jack Forman

The system uses reconfigurable electromechanical building blocks to create structural electronics.

New platform lets anyone rapidly prototype large, sturdy interactive structures

Wevolver: David. So what are we looking at here?David: Thanks. Well, what we have here is an STM32N6. Now, this is a microcontroller-based product. So this is an MCU with an NPU. And what we're doing in this specific example here is object detection and anomaly detection all in one. So we can place a coffee pod or two onto the conveyor belt and we will see that coffee pod has been detected. Now we're also detecting, as I said, anomalies or defect detection. So if I place these coffee pods which have defects in them, we'll see over on the right hand side of the screen where the defects are in a heat map. I'll take this one here, which has some puncture wounds in it. And we'll do the object detection as well as the defects. This is super interesting because this is an MCU based device, extremely low power, and because it's got the MCU and the NPU, we can actually run two distinct models at the same time on the device. Pretty cool.David: All right, and now we're over at a second demo pod here. For this one though, I'm going to actually pass the mic over to my colleague Dmitry, who will walk you through it. Dmitry? Dmitry: Hey. Yeah, I'm Dmitry. And here we see the modal cascade. There are two models running on this particularly powerful device made by Qualcomm. It's 9075, recently announced chip. So we have the object detection model which detects vehicles and then once it's detected, it is being passed to a visual language model. You have a choice of lava, open source, lava and AMG. This is in in-house model by Qualcomm. So we have crafted a prompt here which is the vehicle class, vehicle manufacturer, sign identification. If press generate all, it'll just take a few moments. And keep in mind that both the detection and the visual language model, they both run on this device. It doesn't go anywhere to the clouds, you can see the answers being streamed in real time. Yeah, let's wait for the last one here. Yeah, it gets most of it very correctly. Let's see the last one actually. Yeah, that's all good. And the value proposition for us at Edge Impulse is in fact that you cannot blindly trust visual language models. You use ChatGPT, you know it can make mistakes, it says they are right at the bottom. So in that case, if you really are serious and you want to put this into production, you want to validate your prompt, you want to create it, validate it and check the accuracy against the data that you have, and this is exactly what we are providing at Edge Impulse. So you create your prompt here, you tweak it around, you test it, and then you pass it on to the production model running there. That's about it.David: All right, thanks, Dmitry. Appreciate it. And I think that's going to do it for us here from Embedded World 2025. If you have any questions, be sure to reach out edgeimpulse.com. Thanks.

Unveiling the Future of Embedded AI: Object Anomaly Detection and More at Embedded World 2025

Edge Impulse's Object Anomaly Detection Technology, And Other Innovations at Embedded World 2025

The SeaPerch underwater robot is a popular educational tool for students in grades 5 to 12.  Building and piloting SeaPerch, a remotely operated vehicle (ROV), involves a variety of hand fabrication processes, electronics techniques, and STEM concepts. Through the SeaPerch program, educators and students explore structures, electronics, and underwater dynamics.  “SeaPerch has had a tremendous impact on the fields of ocean science and engineering,” says Andrew Bennett ’85, PhD ’97, MIT SeaGrant education administrator and senior lecturer in the Department of Mechanical Engineering (MechE).The original SeaPerch project was launched by <a href="https://seagrant.mit.edu/">MIT Sea Grant</a> in 2003. In the decades that followed, it quickly spread across the country and overseas, creating a vibrant community of builders. Now under the leadership of RoboNation, SeaPerch continues to thrive with competitions around the world. These competitions introduce challenging real-world problems to foster creative solutions. Some recent topics have included deep sea mining and collecting data on hydrothermal vents.<iframe width="640" height="360" src="https://www.youtube.com/embed/iTNzBFul4C8?&amp;wmode=opaque&amp;rel=0&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe>An underwater robot with a mission Video: Department of Mechanical Engineering<a href="https://seagrant.mit.edu/seaperch2/">SeaPerch II</a>, which has been in development at MIT Sea Grant since 2021, builds on the original program by adding robotics and elements of marine and climate science. It remains a “do-it-yourself” maker project with objectives that are achievable by middle and high school students. Bennett says he hopes SeaPerch II will enable an even greater impact by providing an approachable path to learning more about sensors, robotics, climate science, and more.“What I think is most valuable about it is that it uses hardware store components that need to be cut, waterproofed, connected, soldered, or somehow processed before becoming part of the robot or controller,” says Diane Brancazio ME ’90, K-12 maker team leader for the MIT Edgerton Center, who co-leads the MIT SeaPerch initiative with Bennett. “[It’s] kind of like making a cake from scratch, instead of from a mix — you see what goes into the final product and how it all comes together.”SeaPerch II is a family of modules that allow students and educators to create educational adventures tailored to their particular wants or requirements. Offerings include a pressure and temperature sensing module that can be used on its own; an autonomy module that the students can use to construct a closed-loop automatic depth control system for their SeaPerch; and a lesson module for soft robotic “fingers” that can be configured into grippers, distance sensors, and bump sensors.The basic SeaPerch is a PVC pipe structure with three motors and a tether to a switch box. Through the building process, students learn about buoyancy, structural design, hand fabrication, and electric circuits. SeaPerch II leverages technologies that are more advanced, less expensive, and more accessible than they were when SeaPerch was first conceived. Bennett says SeaPerch II is meant to extend the original SeaPerch program without invalidating any of the existing system.Teagan Sullivan, a third-year student in mechanical engineering, first became involved with the project in January 2023 through an Undergraduate Research Opportunities Program project with MIT Sea Grant. Initially, she continued development of the soft robotics portion of the project, before switching to a more general focus where she worked on frame design for SeaPerch II, making sure components could fit and that stability could be maintained. Later she helped run outreach programs, taking feedback from the students she worked with to help modify designs and make them “more robust and kid-friendly.”“I have been able to see the impact of SeaPerch II on a small scale by working directly with students,” Sullivan says. “I have seen how it encourages creativity, and how it has taught kids that collaboration is the best road to success. SeaPerch II teaches the basics of electronics, coding, and manufacturing, but its best strength is the ability to challenge the way people think and encourage critical thinking.”The team’s vision is to create opportunities for young people to engage in authentic science investigations and engineering challenges, developing a passion for engineering, science, and the aquatic environment. MIT Sea Grant is continuing to develop new SeaPerch II modules, including incorporating land-water communication, salinity and dissolved oxygen sensors, and fluorometers.Sullivan says she hopes the program will reach more students and inspire them to take an interest in engineering while teaching the skills they need to be the next generation of problem-solvers. Brancazio says she hopes this project inspires and prepares young people to work on climate change issues.“Robots are supposed to help people do things they couldn’t otherwise do,” Brancazio says. “SeaPerch is a robot with a mission.”

A SeaPerch with a gripper module drives around the 3,000-gallon tank in the teaching lab at MIT SeaGrant. Photo: Sharmi Shah/MechE

Launched by MIT Sea Grant, SeaPerch and SeaPerch II have had a big impact on young learners interested in ocean science and engineering.

SeaPerch: A robot with a mission

This fresh, newly captured video from Unitree's testing grounds showcases the breakneck speed of humanoid intelligence advancement. Every day brings something thrilling!

This fresh, newly captured video from Unitree's testing grounds showcases the breakneck speed of humanoid intelligence advancement. Every day brings something thrilling!

Movement creates intelligence - Unitree's G1 humanoid robot nails the world's first kip-up!

One year after Unitree H1 (1.8m) pioneered the first standing backflip by an electric humanoid robot (March 2024). Meet the Unitree G1 – now flawlessly conquering an even more challenging standing side flip. (Zero malfunctions/damage occurred during programming and filming.)

One year after Unitree H1 (1.8m) pioneered the first standing backflip by an electric humanoid robot (March 2024). Meet the Unitree G1 – now flawlessly conquering an even more challenging standing side flip. (Zero malfunctions/damage occurred during programming and filming.)

World's First Side-Flipping Humanoid Robot: Unitree G1

It’s difficult to build devices that replicate the fluid, precise motion of humans, but that might change if we could pull a few (literal) strings.At least, that’s the idea behind “cable-driven” mechanisms in which running a string through an object generates streamlined movement across an object’s different parts. Take a robotic finger, for example: You could embed a cable through the palm to the fingertip of this object and then pull it to create a curling motion.While cable-driven mechanisms can create real-time motion to make an object bend, twist, or fold, they can be complicated and time-consuming to assemble by hand. To automate the process, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed an all-in-one 3D printing approach called “Xstrings.” Part design tool, part fabrication method, Xstrings can embed all the pieces together and produce a cable-driven device, saving time when assembling bionic robots, creating art installations, or working on dynamic fashion designs.3D printing approach strings together cable-driven mechanisms for you Video: MIT CSAILIn a <a href="https://groups.csail.mit.edu/hcie/files/research-projects/xstrings/xstrings.pdf" target="_blank">paper</a> to be presented at the 2025 Conference on Human Factors in Computing Systems (CHI2025), the researchers used Xstrings to print a range of colorful and unique objects that included a red walking lizard robot, a purple wall sculpture that can open and close like a peacock’s tail, a white tentacle that curls around items, and a white claw that can ball up into a fist to grab objects.To fabricate these eye-catching mechanisms, Xstrings allows users to fully customize their designs in a software program, sending them to a multi-material 3D printer to bring that creation to life. You can automatically print all the device’s parts in their desired locations in one step, including the cables running through it and the joints that enable its intended motion.MIT CSAIL postdoc and lead author Jiaji Li says that Xstrings can save engineers time and energy, reducing 40 percent of total production time compared to doing things manually. “Our innovative method can help anyone design and fabricate cable-driven products with a desktop bi-material 3D printer,” says Li.<h2>A new twist on cable-driven fabrication</h2>To use the Xstrings program, users first input a design with specific dimensions, like a rectangular cube divided into smaller pieces with a hole in the middle of each one. You can then choose which way its parts move by selecting different “primitives:” bending, coiling (like a spring), twisting (like a screw), or compressing — and the angle of these motions.For even more elaborate creations, users can incorporate multiple primitives to create intriguing combinations of motions. If you wanted to make a toy snake, you could include several twists to create a “series” combo, in which a single cord drives a sequence of motions. To create the robot claw, the team embedded multiple cables into a “parallel” combination, where several strings are embedded, to enable each finger to close up into a fist.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742432637463-mit-Xstrings-diagram.png" style="width:100%px" class="fr-fic fr-dib" />Xstrings facilitates how cables are integrated into the object it’s producing. Users can choose exactly how the strings are secured, including its endpoint, the holes within the structure that the cord passes through, and where you’d pull to operate the device.  Image courtesy of the researchers.Beyond fine-tuning the way cable-driven mechanisms move, Xstrings also facilitates how cables are integrated into the object. Users can choose exactly how the strings are secured, in terms of where the “anchor” (endpoint), “threaded areas” (or holes within the structure that the cord passes through), and “exposed point” (where you’d pull to operate the device) are located. With a robot finger, for instance, you could choose the anchor to be located at the fingertip, with a cable running through the finger and a pull tag exposed at the other end.Xstrings also supports diverse joint designs by automatically placing components that are elastic, compliant, or mechanical. This allows the cable to turn as needed as it completes the device’s intended motion.<h2>Driving unique designs across robotics, art, and beyond</h2>Once users have simulated their digital blueprint for a cable-driven item, they can bring it to life via fabrication. Xstrings can send your design to a fused deposition modeling 3D printer, where plastic is melted down into a nozzle before the filaments are poured out to build structures up layer by layer.Xstrings uses this technique to lay out cables horizontally and build around them. To ensure their method would successfully print cable-driven mechanisms, the researchers carefully tested their materials and printing conditions.For example, the researchers found that their strings only broke after being pulled up and down by a mechanical device more than 60,000 times. In another test, the team discovered that printing at 260 degrees Celsius with a speed of 10-20 millimeters per second was ideal for producing their many creative items.“The Xstrings software can bring a variety of ideas to life,” says Li. “It enables you to produce a bionic robot device like a human hand, mimicking our own gripping capabilities. You can also create interactive art pieces, like a cable-driven sculpture with unique geometries, and clothes with adjustable flaps. One day, this technology could enable the rapid, one-step creation of cable-driven robots in outer space, even within highly confined environments such as space stations or extraterrestrial bases.”The team’s approach offers plenty of flexibility and a noticeable speed boost to fabricating cable-driven objects. It creates objects that are rigid on the outside, but soft and flexible on the inside; in the future, they may look to develop objects that are soft externally but rigid internally, much like humans’ skin and bones. They’re also considering using more resilient cables, and, instead of just printing strings horizontally, embedding ones that are angled or even vertical.Li wrote the paper with Zhejiang University master’s student Shuyue Feng; Tsinghua University master’s student Yujia Liu; Zhejiang University assistant professor and former MIT Media Lab visiting researcher Guanyun Wang; and three CSAIL members: Maxine Perroni-Scharf, an MIT PhD student in electrical engineering and computer science; Emily Guan, a visiting researcher; and senior author Stefanie Mueller, the TIBCO Career Development Associate Professor in the MIT departments of Electrical Engineering and Computer Science and Mechanical Engineering, and leader of the HCI Engineering Group.

The “Xstrings” method can produce a range of colorful and unique objects, like a white tentacle that curls around items and a purple wall sculpture that can open and close. Photo: Mike Grimmett/MIT CSAIL

“Xstrings” method enables users to produce cable-driven objects, automatically assembling bionic robots, sculptures, and dynamic fashion designs.

3D printing approach strings together dynamic objects for you

<h2>Introduction</h2>The Robotics market is experiencing rapid growth globally, with the market value expected to reach $189.3 billion by 2027 [1]. Advancements in automation and  AI-powered vision systems drive this progress. Camera-equipped robotic systems serve as key expansion components within this industry because they improve efficiency and accuracy across multiple fields, including manufacturing, healthcare, logistics, and autonomous vehicle development. These systems use high-resolution imaging and real-time data processing to accomplish tasks that demand precision and adaptability.However, despite their potential, several challenges hinder the performance of these systems. High latency decreases operational efficiency; low-resolution imaging contributes to industrial defect detection errors. Short and inflexible cables, electromagnetic interference (EMI), and high power consumption negatively impact conventional camera systems. These factors influence the functionality and reliability of the systems. Addressing the challenges is essential for achieving the full potential of vision-equipped robotics.Silicon Line, a leading provider of high-performance semiconductor solutions, has developed their Mobile Industry Processor Interface (MIPI) SerDes Integrated Circuits (ICs) to overcome these challenges. These ICs transport native high-resolution images with extremely low latency while allowing longer cable lengths without degrading signals. Silicon Line’s MIPI SerDes ICs enable camera-equipped robotic systems to deliver improved imaging performance while processing data in real time. This results in enhanced operational efficiency, making them more adaptable to industry requirements.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742349844848-ezgif-4d614fd9adfd9d.jpg" /><h2>Challenges in Camera-Equipped Robotic Systems</h2>Robotic vision systems require solutions to multiple challenges for seamless operation in industrial environments. Some of the most common challenges include:<h3>High Latency:</h3>The main operational problem arises from high latency, which causes delays that affect real-time decision-making processes. Applications such as autonomous navigation systems and robotic-assisted surgery require prompt decision-making; even a fraction of a second can lead to suboptimal outcomes.<h3>Low Resolution:</h3>Another challenge that demands immediate attention is implementing high-resolution imaging capabilities. Using low-resolution cameras in industrial robots substantially increases inspection errors, affecting product quality and driving costs. Compressed image data further reduces clarity to the extent that it hinders the effectiveness of AI-based defect detection algorithms. For instance, semiconductor manufacturing experiences financial losses because imprecise imaging can produce defective products.<h3>Short and Inflexible Cables:</h3>Cable limitations also present another significant problem. Transporting high-resolution images requires a very high bandwidth, which limits cable lengths and leads to thick and rigid cables. These cables limit the mobility of robotic arms and other vision-equipped robots, making them difficult to install and maintain. Traditional cables add weight and stiffness to the system, restricting the speed and agility needed for robotic systems to adapt to dynamic environments. The need for 3D camera capability in robotic systems also leads to an increase in cable complexity.<h3>Electromagnetic Interference (EMI):</h3>EMI can also degrade the image quality and hinder AI-driven analysis. Manufacturing environments with several electronic devices are prone to interference, leading to inaccurate object recognition and errors in automated processes, quality control, and safety systems.<h3>High Power Consumption:</h3>High power usage, especially in battery-operated robotic systems, is also a primary concern, as energy-intensive vision systems contribute to more significant operational costs and sustainability challenges. Rectifying these issues is essential for optimizing the efficiency and reliability of robotic vision applications.These challenges affect robotic systems' efficiency and reliability, restricting their potential applications. Overcoming these issues is crucial for industries looking to take full advantage of camera-equipped robotics.<h2>Silicon Line’s MIPI SerDes ICs: A Solution to Camera-Equipped Robotic Challenges</h2>Silicon Line’s MIPI SerDes ICs offer the most efficient solution to these challenges by leveraging advanced serialization and deserialization techniques to optimize high-speed data transfer while maintaining signal integrity. These ICs utilize the industry standard MIPI D-PHY interface, ensuring compatibility with a wide range of imaging and processing components while delivering robust performance in high-speed data transmission applications. These ICs reduce latency by enabling real-time transfer of uncompressed and unconverted high-resolution images, keeping robotic systems in a position to make accurate, split-second decisions within automated navigation and robotic surgical treatments.These ICs also enhance image recognition and support ultra-high-definition image transmission without compression, preserving crucial details for defect detection and quality control through AI. They increase the strength of Machine learning (ML)/ Deep Learning (DL) algorithms for detecting defects by eliminating lossy image processing. Thus increasing the accuracy of inspections on industrial sites such as semiconductor manufacturing and precision assembly lines.Silicon Line’s ICs overcome the cable's limitations by supporting optical and copper based transmission over long distances and reducing cable thickness and weight. The serialization process means the 10-wire MIPI D-PHY interface is serialized to just two wires or one optical fiber. The latest Silicon Line products can also transport dual camera images over the same single fiber or two copper wires. The ICs facilitate greater robotic mobility, flexibility, and accuracy. Additionally, Silicon Line’s extremely low-power circuit design saves power, making vision-enabled robotic systems more sustainable and cost-effective. Their compatibility with various AI &amp; edge computing platforms ensures seamless integration, using real-time data analysis and decision-making processes in robotics applications across industries. <img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742349964256-1742349964256.png" /><h2>Real-World Applications of MIPI SerDes ICs in Robotics</h2>Silicon Line’s MIPI SerDes ICs have several applications across various industries. Here are a few examples:<h3>Humanoid Robotics:</h3>Humanoid robots with high-resolution cameras need lightweight image processing to frequently perform complicated tasks such as navigation and object manipulation. Implementing MIPI SerDes ICs allows these robots to operate with greater precision and responsiveness in industries and healthcare. These ICs aid humanoid robots in improving interaction with humans in service robotics and healthcare for elderly care applications.<h3>Surgical Robotics:</h3>In medical robotics, ultra-high-definition imaging is required for minimally invasive procedures. MIPI SerDes ICs allow transmitting real-time images, giving surgeons clear and precise visuals during operative procedures. This dramatically increases accuracy and patient safety, especially in delicate procedures such as neurosurgery or robotic-assisted laparoscopy.<h3>Factory Automation and Quality Control:</h3>Manufacturing plants employ vision systems for the detection of defects and quality control. Integrating high-resolution imaging with AI-based analysis decreases defect rates significantly. MIPI SerDes ICs allow high-resolution native data transmission, enabling manufacturers to implement automated inspection systems with increased precision and reliability. This improves productivity and reduces material waste, resulting in cost savings and enhanced sustainability.<h2>Autonomous Guided Vehicles (AGVs)</h2>AGVs are crucial for logistics and warehouse automation, where real-time navigation and obstacle detection are essential. High-speed image transmission makes AGVs move efficiently, reducing the risk of collisions and enhancing overall workflow efficiency. Silicon Line’s MIPI SerDes ICs increase the performance of these vehicles by enabling real-time data processing and longer, flexible cable lengths.<h2>Collaborative Robots (Cobots)</h2>Cobots increase productivity, precision, and safety by working alongside human operators. They need real-time object detection and decision-making, requiring high-resolution data transfer and real-time handling. Silicon ICs, especially the MIPI SerDes ICs, enhance cobot's efficiency and reliability through high imaging resolution, reduced latency, and low power dissipation. This enables cobots to work flawlessly in manufacturing, healthcare, and logistics, where adaptability and high accuracy are paramount.<h3>Conclusion:</h3>Camera-equipped robotic systems are reshaping industries, but challenges like high latency, low resolution, inflexible and short cables often limit their potential. The advancement in silicon IC technology, especially MIPI SerDes ICs, has significantly transformed the performance of camera-enabled robotic systems. These innovations have resolved the key issues associated with conventional systems. These ICs have improved the performance and reliability of robotics in almost all sectors by allowing high-speed data transmission, real-time image processing, and energy-efficient operation.Continuous advancements in semiconductor technology will shape the future of robotics. Developing ultra-low power ICs, AI-optimized processors, and edge-computing solutions will further enhance the autonomy and efficiency of the robotic systems. Furthermore, integrating 3D imaging, hyperspectral vision, and neuromorphic computing will allow robots to perceive and interpret their environments accurately.With industries moving towards intelligent automation, the fusion of the latest silicon ICs and AI-driven robotics will result in more advanced, adaptable, and sustainable solutions. These innovations will increase the functions of camera-based robotic automation, transform the future of robotics and automation, and enhance productivity, safety, and operational efficiency across diverse sectors. Silicon Line invites professionals from all stakeholders to explore these advancements further.<h1>References:</h1>[1]    Advantech, “Enabling Smart Robotics with Single-Board Computing.”[2]    P. Lu, K. Yuan, and W. Zou, “A High Performance Low Power Consumption Robot Vision System,” in Third International Conference on Natural Computation (ICNC 2007) Vol V, IEEE, Aug. 2007, pp. 171–175. doi: 10.1109/ICNC.2007.42.[3]    Silicon Line, “Silicon Line.” Accessed: Feb. 21, 2025. [Online]. Available: https://www.silicon-line.com/

Camera-equipped robotic systems serve as key expansion components within this industry because they improve efficiency and accuracy across multiple fields, including manufacturing, healthcare, logistics, and autonomous vehicle development.

Highly Accurate Object Recognition: MIPI Serialization ICs in  Camera-Equipped Robotic Systems

The use of robots alongside humans is increasing rapidly, making the flexibility of these machines an important safety feature. A robot's ability to adapt flexibly to its surroundings makes the difference between smooth operation and potential hazards, such as collisions with people nearby. The German Aerospace Center (Deutsche Zentrum für Luft- und Raumfahrt; DLR) has established itself as a pioneer in the field of cobots – collaborative robots that work in close cooperation with humans. Until now, the goal of cobot research has been to design robots that behave like a mechanical spring – if moved towards a desired position, they will always return to their original position. DLR researchers have now presented a more advanced concept in the journal Science Robotics. Their approach allows robots to yield without returning to their original position like a spring. This behaviour is similar to that of humans in joint practical tasks that need to be completed, where one person takes the lead while the other remains flexible and compliant.Michael Panzirsch, researcher at the DLR Institute of Robotics and Mechatronics, explains: "The new elasto-plastic approach makes human-robot collaboration much easier, as the robot can now clearly distinguish between its own programmed movements and external influences from the environment. The robot should only react plastically to the influence of the environment, meaning it should move out of the way and remain in that spot."In addition, the innovative controller generally simplifies interactions with objects that have a complex movement. For example, a hinged door can only be opened by rotating it around its axis. The robot follows the correct direction of movement – the circular motion around the hinges – almost automatically via the controller and without the need for an additional model of the door. This is comparable to a person opening a door with their eyes closed, simply pulling on the door handle and automatically adapting to the rotating motion in a plastically compliant manner.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742345164286-image-1000-a8507f2c4983a98356706e6648326803.jpeg" />Two robotic hands cooperating to handle a tool While the arm of the DLR humanoid robot (right) independently carries an object, an astronaut controls the ESA Interact Rover's arm (left). The astronaut guides the object to the target position. The new approach allows for the humanoid to maintain the object's rotation while yielding to the rover arm during linear movements.  Credit: DLR (CC BY-NC-ND 3.0)<h2>Applications in space research and care</h2>The DLR Institute of Robotics and Mechatronics has been researching sensitive robots for decades. It has developed a robotic arm that stands out from conventional rigid industrial robots thanks to its remarkable flexibility. The elasto-plastic control approach was successfully tested in space as early as January 2024. The Surface Avatar project series is investigating the teleoperation of several robotic avatars on a planetary surface. The focus here is on the successful cooperation between a diverse team of robots performing collaborative tasks. "For the first time, our controller enabled cooperation between a rover from the European Space Agency's Human Robot Interaction Laboratory and a humanoid DLR robot controlled by an astronaut on the International Space Station," explains Neal Y. Lii, Scientific Director of the Surface Avatar programme.But the versatility of the technology is not limited to space. In the care sector, the elasto-plastic controller is proving to be a valuable aid. As part of the SMiLE project (Services for People with Living Conditions and Restrictions; Servicerobotik für Menschen in Lebenssituationen mit Einschränkungen), DLR researchers like Jörn Vogel are developing concepts for assistance systems designed to effectively support people with physical limitations and those in need of care in everyday life. The elasto-plastic controller not only facilitates cooperation between humans and robots but also enables the robot to seamlessly slip into an assistant role without requiring complex additional sensor technology.Advances in robotics, particularly in terms of compliance and adaptability, not only accelerate the integration of robots into everyday life but also increase safety and effectiveness. In a future where humans and machines work ever more closely together, it will be crucial for robots to not only react but also interpret our needs and idiosyncrasies, thereby mirroring human capabilities.

Remote-controlled from the ISS − DLR Rollin' Justin robot and the ESA Interact Rover. DLR's autonomous humanoid robot Rollin' Justin (right) and the ESA Interact Rover (left) being controlled from the International Space Station ISS. They are cooperating on the handling of a tool. Credit: DLR (CC BY-NC-ND 3.0)

DLR's concept allows collaborative robots to yield without being forced back to their original position like a mechanical spring.

The art of compliant robotics

<html><head></head><body>Spinal cord injuries are life-altering, often leaving individuals with severe mobility impairments. While rehabilitation robotics—devices that guide movement during therapy—have improved training for those with spinal cord injuries, their effectiveness remains limited. Without active muscle engagement, robotic-assisted movement alone does not sufficiently retrain the nervous system.<iframe width="640" height="360" src="https://www.youtube.com/embed/KnyHjGcDnME?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0" allowfullscreen="" class="fr-draggable" data-lf-form-tracking-inspected-kn9eq4roawkarlvp="true" data-lf-yt-playback-inspected-kn9eq4roawkarlvp="true" data-lf-vimeo-playback-inspected-kn9eq4roawkarlvp="true"></iframe> A team at .<a href="https://www.neurorestore.swiss/" target="_blank" rel="noopener">NeuroRestore</a>, led by Grégoire Courtine and Jocelyne Bloch, has now developed a system that seemlessly integrates an implanted spinal cord neuroprosthesis with rehabilitation robotics. The researchers’ device delivers well-timed electrical pulses to stimulate muscles in harmony with robotic movements, resulting in natural and coordinated muscle activity during therapy. The neuroprosthetics innovation leveraged the the robotic expertise of <a href="https://www.epfl.ch/labs/biorob/">Professor Auke Ijspeert’s lab at EPFL</a>. This advancement not only enhances immediate mobility but also fosters long-term recovery.“The seamless integration of spinal cord stimulation with rehabilitation or recreational robotics will accelerate the deployment of this therapy into the standard of care and the community of people with spinal cord injury,” says Courtine. This adaptability ensures that rehabilitation professionals can incorporate this technology into existing rehabilitation protocols worldwide. Combining therapies also presents significant challenges, as each requires precise synchronization. Spinal cord stimulation strategies must be modulated in both space and time to match the patient’s movement, and integrating them with widely used robotic rehabilitation systems requires a flexible and adaptable framework.The technology relies on a fully implanted spinal cord stimulator that delivers biomimetic electrical epidural stimulation (electrical epidural stimulation). Unlike traditional functional electrical stimulation, this method activates motor neurons more efficiently by mimicking natural nerve signals.The researchers integrated electrical epidural stimulation with various robotic rehabilitation devices—including treadmills, exoskeletons, and stationary bikes—ensuring that stimulation is precisely timed with each phase of movement. The system uses wireless sensors to detect limb motion and automatically adjust stimulation in real time, allowing for a seamless user experience.In a proof-of-concept study involving five individuals with spinal cord injuries, the combination of robotics and electrical epidural stimulation resulted in immediate and sustained muscle activation. Not only did participants regain the ability to engage muscles during robotic-assisted therapy, but some also improved their voluntary movements even after the stimulation was turned off.The researchers also worked closely with rehabilitation centers to test how well the stimulation system integrated with widely used robotic devices. “We visited multiple rehabilitation centers to test our stimulation technology with the robotic systems they routinely use, and it was incredibly rewarding to witness their enthusiasm,” say .NeuroRestore researcher Nicolas Hankov and BioRob researcher Miroslav Caban, the study’s first authors. “Seeing firsthand how seamlessly our approach integrates with existing rehabilitation protocols reinforces its potential to transform care for people with spinal cord injury by providing a technological framework that is easy to adopt and deploy across multiple rehabilitation environments.”The study also showed the potential of this approach beyond clinical settings, as participants used the system to walk with a rollator and cycle outdoors, validating its real-world impact.This innovative technology offers new hope for individuals with spinal cord injuries, presenting a more effective rehabilitation approach than robotics alone. By making rehabilitation more dynamic and engaging, it has the potential to significantly enhance recovery outcomes. Future clinical trials will be needed to establish long-term benefits, but the initial results suggest that integrating neuroprosthetics with rehabilitation robotics could redefine mobility restoration after paralysis.List of contributors<ul><li>EPFL Neuro X</li><li>Lausanne University Hospital (CHUV) and University of Lausanne (UNIL)</li><li>Defitech Center for Interventional Neurotherapies (.NeuroRestore)</li><li>EPFL Biorobotics Laboratory</li><li>ONWARD Medical</li><li>Bern University of Applied Sciences</li><li>VAMED Management and Service Switzerland AG</li><li>ETH Zurich Sensory-Motor Systems Lab</li><li>University of Zurich Spinal Cord Injury Center</li><li>Hocoma AG</li><li>Medtronic</li><li>Oxford University</li><li>GBY (Go-by-Yourself) SA</li><li>Université de Bordeaux Institut des Maladies Neurodégénératives</li><li>Zurich University of Applied Sciences (ZHAW)</li><li>MyoSwiss AG</li></ul><a href="https://www.dropbox.com/scl/fo/mrs3q6p0swmb2ojq4hz9r/APSxvOKXuBsk7FztEVNrhZA?rlkey=im02yangj4ghnadhi7g7eqvei&amp;e=1&amp;st=4p8m3tfd&amp;dl=0" target="_blank" rel="noopener">Press kit</a><hr>ReferencesNicolas Hankov, Miroslav Caban, Robin Demesmaeker, Margaux Roulet, Salif Komi, Michele Xiloyannis, Anne Gehrig, Camille Varescon, Martina Rebeka Spiess, Serena Maggioni, Chiara Basla, Gleb Koginov, Florian Haufe, Marina D’Ercole, Cathal Harte, Sergio D. Hernandez-Charpak, Aurelie Paley, Manon Tschopp, Natacha Herrmann, Nadine Intering, Edeny Baaklini, Francesco Acquati, Charlotte Jacquet, Anne Watrin, Jimmy Ravier, Frédéric Merlos, Grégoire Eberlé, Katrien Van den Keybus, Hendrik Lambert, Henri Lorach, Rik Buschman, Nicholas Buse, Timothy Denison, Dino De Bon, Jaime E. Duarte, Robert Riener, Auke Ijspeert, Fabien Wagner, Sebastian Tobler, Leonie Asboth, Joachim Von Zitzewitz, Jocelyne Bloch, Grégoire Courtine. Augmenting rehabilitation robotics with spinal cord neuromodulation: a proof of concept. Science Robotics 12 March 2025. DOI:&nbsp;<a href="https://doi.org/10.1126/scirobotics.adn5564" target="_blank" rel="noopener">10.1126/scirobotics.adn5564</a></body></html>

Common robotic devices to safely automate and augment gait rehabilitation. © .NeuroRestore/EPFL/CHUV 2025

Scientists at .Neurorestore (EPFL/CHUV/UNIL) have developed an approach that combines rehabilitation robotics with spinal cord stimulation to restore movement in people with spinal cord injuries. The technology enhances rehabilitation and enables activities like cycling and walking outdoors.

Robotics and spinal stimulation restore movement in paralysis

<h3 id="isPasted">The Importance of Edge AI Deployments</h3>Edge AI refers to the practice of running artificial intelligence models directly on edge devices, such as sensors, smartphones, or industrial machines, rather than relying solely on centralized cloud servers. Some Arduino boards based on a microcontroller – such as those in the Portenta family (<a href="https://www.arduino.cc/pro/hardware-product-portenta-h7" target="_blank">Portenta H7</a>, <a href="https://www.arduino.cc/pro/hardware-product-portenta-c33" target="_blank">Portenta C33</a>) or <a href="https://www.arduino.cc/pro/hardware-arduino-opta/" target="_blank">Opta</a> – allow running Edge AI natively, leveraging tools like Tiny Machine learning. This approach offers several advantages:<ul><li>Reduced Latency: Processing data locally enables real-time decision-making, which is essential for applications like autonomous vehicles and industrial automation.</li><li>Enhanced Privacy: Keeping data on-device minimizes the risk of exposure, addressing privacy concerns in sectors like healthcare and finance.</li><li>Bandwidth Efficiency: Local processing decreases the need to transmit large volumes of data to the cloud, reducing bandwidth usage and associated costs.</li></ul>However, deploying AI models on edge devices presents challenges, including limited computational resources and the need for efficient model management. <h3>Leveraging Docker for Edge AI </h3>Docker containers encapsulate applications and their dependencies into isolated environments, ensuring consistency across deployments. This is particularly beneficial for edge AI due to:<ul><li>Simplified Deployment: Containers bundle the application code, libraries, and configurations, eliminating dependency conflicts and streamlining the deployment process.</li><li>Portability: Docker ensures that applications run uniformly across different environments, making it easier to deploy models on various edge devices.</li><li>Resource Efficiency: Containers are lightweight compared to traditional virtual machines, making them suitable for devices with constrained resources.</li></ul>Implementing Docker for edge AI deployments can lead to more manageable and scalable solutions, as it abstracts the underlying hardware and provides a uniform interface for application deployment.<h3>Implementing Edge Impulse Models with Docker </h3>To deploy an Edge Impulse model using Docker on an embedded Linux platform, follow these steps:<ol><li>Model Training: Develop and optimize your machine learning model using the Edge Impulse platform, which provides tools for collecting data, designing algorithms, and validating performance.</li><li>Container Setup: Utilize a pre-configured Docker image that includes the necessary runtime libraries for your model. This involves creating a Dockerfile that specifies the base image, installs dependencies, and copies the model into the container.</li><li>Deployment on Embedded Linux: Execute the containerized model on your target hardware, leveraging its processing capabilities for efficient inference. Docker’s portability ensures that the model runs consistently across different devices.</li></ol>This methodology simplifies AI implementation, making it accessible for engineers involved in industrial automation, predictive maintenance, and intelligent sensing applications.<h3>The Role of Portenta X8 in Edge AI Deployments  <iframe id="635a948941249700089f8799" width="250" height="440" src="https://wevolver.com/iframe/specs/arduino-portenta-x8?iframe=true" frameborder="0" scrolling="no"></iframe></h3>For developers looking for a hardware platform that efficiently bridges embedded and industrial applications, the <a href="https://www.arduino.cc/pro/hardware-product-portenta-x8/" target="_blank">Arduino Portenta X8</a> offers a compelling solution. With its combination of embedded Linux and microcontroller capabilities, it provides:<ul><li>Multi-Core Processing: A powerful heterogeneous architecture that enables real-time AI inference while handling additional processing tasks.</li><li>Linux Compatibility: The ability to run containerized applications seamlessly, offering flexibility for edge AI implementations.</li><li>Connectivity Options: Built-in support for Wi-Fi, Bluetooth, and Ethernet, essential for real-time data transmission and remote management.</li></ul>By leveraging the Portenta X8’s capabilities alongside Docker, developers can streamline the deployment of machine learning models while maintaining security, scalability, and performance at the edge.<h3><iframe width="640" height="360" src="https://www.youtube.com/embed/c2nz1wLIp5g?&amp;wmode=opaque&amp;enablejsapi=1&amp;origin=https://www.wevolver.com" frameborder="0"></iframe></h3><h3>Get Started with the Full Guide</h3>To deploy your own Edge Impulse models using Docker, explore the step-by-step tutorial <a href="https://docs.arduino.cc/tutorials/portenta-x8/edge-ai-docker-container/" target="_blank">here</a> and begin integrating intelligence into your edge devices today.

The Arduino Portenta X8 combines embedded Linux and microcontroller capabilities.

Deploying machine learning models at the edge is crucial for real-time data processing in industrial applications. This article explores how Docker simplifies Edge AI deployment on the Arduino Portenta X8, enabling scalable and secure AI implementations on embedded Linux systems.

Deploying Edge AI Models with Docker Containers

<html><head></head><body>We move thanks to coordination among many skeletal muscle fibers, all twitching and pulling in sync. While some muscles align in one direction, others form intricate patterns, helping parts of the body move in multiple ways.In recent years, scientists and engineers have looked to muscles as potential actuators for “biohybrid” robots — machines powered by soft, artificially grown muscle fibers. Such bio-bots could squirm and wiggle through spaces where traditional machines cannot. For the most part, however, researchers have only been able to fabricate artificial muscle that pulls in one direction, limiting any robot’s range of motion.Now MIT engineers have developed a <a href="https://pubs.rsc.org/en/content/articlelanding/2025/bm/d4bm01017e" target="_blank">method to grow artificial muscle tissue</a> that twitches and flexes in multiple coordinated directions. As a demonstration, they grew an artificial, muscle-powered structure that pulls both concentrically and radially, much like how the iris in the human eye acts to dilate and constrict the pupil.The researchers fabricated the artificial iris using a new “stamping” approach they developed. First, they 3D-printed a small, handheld stamp patterned with microscopic grooves, each as small as a single cell. Then they pressed the stamp into a soft hydrogel and seeded the resulting grooves with real muscle cells. The cells grew along these grooves within the hydrogel, forming fibers. When the researchers stimulated the fibers, the muscle contracted in multiple directions, following the fibers’ orientation.“With the iris design, we believe we have demonstrated the first skeletal muscle-powered robot that generates force in more than one direction. That was uniquely enabled by this stamp approach,” says Ritu Raman, the Eugene Bell Career Development Professor of Tissue Engineering in MIT’s Department of Mechanical Engineering.The team says the stamp can be printed using tabletop 3D printers and fitted with different patterns of microscopic grooves. The stamp can be used to grow complex patterns of muscle — and potentially other types of biological tissues, such as neurons and heart cells — that look and act like their natural counterparts.“We want to make tissues that replicate the architectural complexity of real tissues,” Raman says. “To do that, you really need this kind of precision in your fabrication.”She and her colleagues <a href="https://pubs.rsc.org/en/content/articlelanding/2025/bm/d4bm01017e" target="_blank">published their open-access results Friday in the journal Biomaterials Science</a>. Her MIT co-authors include first author Tamara Rossy, Laura Schwendeman, Sonika Kohli, Maheera Bawa, and Pavankumar Umashankar, along with Roi Habba, Oren Tchaicheeyan, and Ayelet Lesman of Tel Aviv University in Israel.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1742260285838-MIT-MuscleStamp-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">The researchers developed a new “stamping” approach. First, they 3D-printed a small, handheld stamp (top images) patterned with microscopic grooves, each as small as a single cell. Then they pressed the stamp into a soft hydrogel and seeded the resulting grooves with real muscle cells. The cells grew along these grooves within the hydrogel, forming fibers (bottom image). Image: Courtesy of the researchers<h2>Training space</h2>Raman’s lab at MIT aims to engineer biological materials that mimic the sensing, activity, and responsiveness of real tissues in the body. Broadly, her group seeks to apply these bioengineered materials in areas from medicine to machines. For instance, she is looking to fabricate artificial tissue that can restore function to people with neuromuscular injury. She is also exploring artificial muscles for use in soft robotics, such as muscle-powered swimmers that move through the water with fish-like flexibility.Raman has previously developed what could be seen as gym platforms and workout routines for lab-grown muscle cells. She and her colleagues designed a <a href="https://news.mit.edu/2023/wobbly-gel-mat-trains-muscle-cells-work-together-1020" target="_blank">hydrogel “mat”</a> that encourages muscle cells to grow and fuse into fibers without peeling away. She also derived a way to “exercise” the cells by genetically engineering them to twitch in response to pulses of light. And, her group has come up with ways to direct muscle cells to grow in long, parallel lines, similar to natural, striated muscles. However, it’s been a challenge, for her group and others, to design artificial muscle tissue that moves in multiple, predictable directions.“One of the cool things about natural muscle tissues is, they don’t just point in one direction. Take for instance, the circular musculature in our iris and around our trachea. And even within our arms and legs, muscle cells don’t point straight, but at an angle,” Raman notes. “Natural muscle has multiple orientations in the tissue, but we haven’t been able to replicate that in our engineered muscles.”<h2>Muscle blueprint</h2>In thinking of ways to grow multidirectional muscle tissue, the team hit on a surprisingly simple idea: stamps. Inspired in part by the classic Jell-O mold, the team looked to design a stamp, with microscopic patterns that could be imprinted into a hydrogel, similar to the muscle-training mats that the group has previously developed. The patterns of the imprinted mat could then serve as a roadmap along which muscle cells might follow and grow.“The idea is simple. But how do you make a stamp with features as small as a single cell? And how do you stamp something that’s super soft? This gel is much softer than Jell-O, and it’s something that’s really hard to cast, because it could tear really easily,” Raman says.The team tried variations on the stamp design and eventually landed on an approach that worked surprisingly well. The researchers fabricated a small, handheld stamp using high-precision printing facilities in MIT.nano, which enabled them to print intricate patterns of grooves, each about as wide as a single muscle cell, onto the bottom of the stamp. Before pressing the stamp into a hydrogel mat, they coated the bottom with a protein that helped the stamp imprint evenly into the gel and peel away without sticking or tearing.As a demonstration, the researchers printed a stamp with a pattern similar to the microscopic musculature in the human iris. The iris comprises a ring of muscle surrounding the pupil. This ring of muscle is made up of an inner circle of muscle fibers arranged concentrically, following a circular pattern, and an outer circle of fibers that stretch out radially, like the rays of the sun. &nbsp;Together, this complex architecture acts to constrict or dilate the pupil.Once Raman and her colleagues pressed the iris pattern into a hydrogel mat, they coated&nbsp;the mat with cells that they genetically engineered to respond to light. Within a day, the cells fell into the microscopic grooves and began to fuse into fibers, following the iris-like patterns and eventually growing into a whole muscle, with an architecture and size similar to a real iris.When the team stimulated the artificial iris with pulses of light, the muscle contracted in multiple directions, similar to the iris in the human eye. Raman notes that the team’s artificial iris is fabricated with skeletal muscle cells, which are involved in voluntary motion, whereas the muscle tissue in the real human iris is made up of smooth muscle cells, which are a type of involuntary muscle tissue. They chose to pattern skeletal muscle cells in an iris-like pattern to demonstrate the ability to fabricate complex, multidirectional muscle tissue.“In this work, we wanted to show we can use this stamp approach to make a ‘robot’ that can do things that previous muscle-powered robots can’t do,” Raman says. “We chose to work with skeletal muscle cells. But there’s nothing stopping you from doing this with any other cell type.”She notes that while the team used precision-printing techniques, the stamp design can also be made using conventional tabletop 3D printers. Going forward, she and her colleagues plan to apply the stamping method to other cell types, as well as explore different muscle architectures and ways to activate artificial, multidirectional muscle to do useful work.“Instead of using rigid actuators that are typical in underwater robots, if we can use soft biological robots, we can navigate and be much more energy-efficient, while also being completely biodegradable and sustainable,” Raman says. “That’s what we hope to build toward.”This work was supported, in part, by the U.S. Office of Naval Research, the U.S. Army Research Office, the U.S. National Science Foundation, and the U.S. National Institutes of Health.</body></html>

MIT engineers grew an artificial, muscle-powered structure that pulls both concentrically and radially, much like how the iris in the human eye acts to dilate and constrict the pupil. Image: Courtesy of the researchers

MIT engineers developed a way to grow artificial tissues that look and act like their natural counterparts.

Artificial muscle flexes in multiple directions, offering a path to soft, wiggly robots

<html><head></head><body>Imagine that a robot is helping you clean the dishes. You ask it to grab a soapy bowl out of the sink, but its gripper slightly misses the mark.Using a new framework developed by MIT and NVIDIA researchers, you could correct that robot’s behavior with simple interactions. The method would allow you to point to the bowl or trace a trajectory to it on a screen, or simply give the robot’s arm a nudge in the right direction.Unlike other methods for correcting robot behavior, this technique does not require users to collect new data and retrain the machine-learning model that powers the robot’s brain. It enables a robot to use intuitive, real-time human feedback to choose a feasible action sequence that gets as close as possible to satisfying the user’s intent.When the researchers tested their framework, its success rate was 21 percent higher than an alternative method that did not leverage human interventions.In the long run, this framework could enable a user to more easily guide a factory-trained robot to perform a wide variety of household tasks even though the robot has never seen their home or the objects in it.“We can’t expect laypeople to perform data collection and fine-tune a neural network model. The consumer will expect the robot to work right out of the box, and if it doesn’t, they would want an intuitive mechanism to customize it. That is the challenge we tackled in this work,” says Felix Yanwei Wang, an electrical engineering and computer science (EECS) graduate student and lead author of a <a href="https://arxiv.org/pdf/2411.16627" target="_blank">paper on this method</a>.His co-authors include Lirui Wang PhD ’24 and Yilun Du PhD ’24; senior author Julie Shah, an MIT professor of aeronautics and astronautics and the director of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL); as well as Balakumar Sundaralingam, Xuning Yang, Yu-Wei Chao, Claudia Perez-D’Arpino PhD ’19, and Dieter Fox of NVIDIA. The research will be presented at the International Conference on Robots and Automation.<img src="https://s3-us-west-1.amazonaws.com/wevolver-project-images/froala%2F1741875298807-MIT-Policy-Steering-02-press.jpg" style="width: 100%px;" class="fr-fic fr-dib">The interactive system developed by MIT researchers allows laypeople to easily correct a robot’s behavior using simple interactions. For instance, a user can guide a robot’s actions by pointing to an object on a screen, as demonstrated here by graduate student Felix Yanwei Wang.&nbsp; Credit: Melanie Gonick, MIT<h2>Mitigating misalignment</h2>Recently, researchers have begun using pre-trained generative AI models to learn a “policy,” or a set of rules, that a robot follows to complete an action. Generative models can solve multiple complex tasks.During training, the model only sees feasible robot motions, so it learns to generate valid trajectories for the robot to follow.While these trajectories are valid, that doesn’t mean they always align with a user’s intent in the real world. The robot might have been trained to grab boxes off a shelf without knocking them over, but it could fail to reach the box on top of someone’s bookshelf if the shelf is oriented differently than those it saw in training.To overcome these failures, engineers typically collect data demonstrating the new task and re-train the generative model, a costly and time-consuming process that requires machine-learning expertise.Instead, the MIT researchers wanted to allow users to steer the robot’s behavior during deployment when it makes a mistake.But if a human interacts with the robot to correct its behavior, that could inadvertently cause the generative model to choose an invalid action. It might reach the box the user wants, but knock books off the shelf in the process.“We want to allow the user to interact with the robot without introducing those kinds of mistakes, so we get a behavior that is much more aligned with user intent during deployment, but that is also valid and feasible,” Wang says.Their framework accomplishes this by providing the user with three intuitive ways to correct the robot’s behavior, each of which offers certain advantages.First, the user can point to the object they want the robot to manipulate in an interface that shows its camera view. Second, they can trace a trajectory in that interface, allowing them to specify how they want the robot to reach the object. Third, they can physically move the robot’s arm in the direction they want it to follow.“When you are mapping a 2D image of the environment to actions in a 3D space, some information is lost. Physically nudging the robot is the most direct way to specifying user intent without losing any of the information,” says Wang.<h2>Sampling for success</h2>To ensure these interactions don’t cause the robot to choose an invalid action, such as colliding with other objects, the researchers use a specific sampling procedure. This technique lets the model choose an action from the set of valid actions that most closely aligns with the user’s goal.“Rather than just imposing the user’s will, we give the robot an idea of what the user intends but let the sampling procedure oscillate around its own set of learned behaviors,” Wang explains.This sampling method enabled the researchers’ framework to outperform the other methods they compared it to during simulations and experiments with a real robot arm in a toy kitchen.While their method might not always complete the task right away, it offers users the advantage of being able to immediately correct the robot if they see it doing something wrong, rather than waiting for it to finish and then giving it new instructions.Moreover, after a user nudges the robot a few times until it picks up the correct bowl, it could log that corrective action and incorporate it into its behavior through future training. Then, the next day, the robot could pick up the correct bowl without needing a nudge.“But the key to that continuous improvement is having a way for the user to interact with the robot, which is what we have shown here,” Wang says.In the future, the researchers want to boost the speed of the sampling procedure while maintaining or improving its performance. They also want to experiment with robot policy generation in novel environments.</body></html>

Graduate student Felix Yanwei Wang nudges a robotic arm that is manipulating a bowl in a toy kitchen set up in the group’s lab. Using the framework Wang and his collaborators developed, slightly nudging a robot is one way to correct its behavior. Credit: Melanie Gonick, MIT

New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

Robotic helper making mistakes? Just nudge it in the right direction

<html><head></head><body>Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot. From this, it calculates the fastest route. Similar algorithms could also be used for humanoid robots or autonomous driving to enable safe interaction between robots and humans.The little robot weaves its way through crowds of people on its wheels like a human. To make this possible, researchers from TUM Professor Angela Schoellig's Learning Systems and Robotics Lab have combined computing power, sensors and mathematical skills. ‘Our robot models the way people will react to its movements to plan its paths. This is the big difference to other approaches that typically ignore this interaction,’ explains Prof. Schoellig.<h2>New route calculated ten times per second</h2>A lidar system constantly sends laser beams into the surroundings, measures their reflections, and uses them to build a precise 360-degree map of what the robot sees. A special focus is placed on people walking around nearby. At the same time, sensors in the wheels measure the robot's own speed and the distances traveled. A computer processes this information, calculates the estimated distances people will cover in the next two seconds, and simultaneously plans the optimum route to the destination. ‘Our robot adjusts its route ten times a second while simultaneously recognizing people's paths,’ explains TUM researcher Sepehr Samavi.<h2>System learns from data based on people in crowds</h2>The robot, which Sepehr Samavi has named ‘Jack’, learns behavioral patterns from humans so that it does not constantly stop due to the risk of collisions. ‘Our mathematical model, on which the planning algorithm is based, was derived from human movements and translated into equations,’ explains Professor Schoellig. For Jack's decision, this means that he does not stop immediately as soon as a person approaches him. He takes into account that people will adapt to the situation, react and change their route slightly so that they do not collide with him. If someone remains on a collision course contrary to expectations, the robot changes its plans quickly and takes a different route - but does not stop.&nbsp;The researchers also incorporate data sets that show people's behavior in crowds. The robot, which has already been used outside of the laboratory, is constantly learning and becoming more human-like: ‘Jack knows his destination, observes people and sees where they are going to constantly optimize his own paths,’ says Prof. Schoellig, ‘almost like a human being.’&nbsp;The TUM researchers have already reached the third evolutionary stage with the new algorithm. Instead of ‘only’ reacting to a situation (stage 1) or ‘merely’ predicting the movements of oncoming people (stage 2), the TUM robot is interactive (stage 3). ‘On the one hand, it predicts other people's movements, but it also manages to influence these people through its behavior and simultaneously avoid collisions,’ explains researcher Sepehr Samavi. &nbsp;&nbsp;<h2>Development for use in autonomous driving</h2>It is precisely such interactive scenarios that form the bottleneck in autonomous driving, says Prof Schoellig. For example, if a vehicle starts accelerating on a highway on-ramp, many drivers will change lanes or tap the brakes. The new approach makes it possible to consider the reaction of others in such a scenario. However, the researchers are initially looking at applications in delivery robots or with wheelchair users. The advantage: these vehicles can reach their destination independently and reliably. Even humanoid robots could benefit from the new algorithms. However, the intelligent vehicle has one decisive disadvantage: ‘A moving robot can simply stop if necessary - humanoids are still quite wobbly and quickly lose their balance,’ says Prof. Schoellig.<hr>PublicationsSepehr Samavi, James R. Han, Florian Shkurti, and Angela P. Schoellig; SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization; IEEE Transaction on Robotics; 2025; <a href="https://ieeexplore.ieee.org/document/10726864" target="_blank" rel="noreferrer">https://ieeexplore.ieee.org/document/10726864</a><hr>Further information and links- Prof. Angela Schoellig heads the Chair of Safety, Performance, and Reliability for Learning Systems at the TUM School of Computation, Information and Technology. She is responsible for international relations on the board of the Munich Institute of Robotics and Machine Intelligence (MIRMI) and also coordinates the Robotics Institute Germany, which is funded by the Federal Ministry of Education and Research (BMBF) – <a href="https://www.robotics-institute-germany.de/" target="_blank" rel="noreferrer">https://www.robotics-institute-germany.de/</a>. She is also a member of the Munich Data Science Institute (MDSI) at TUM. &nbsp;&nbsp;- TUM researcher Sepehr Samavi introduces to Jack: <a href="https://youtu.be/7pxZhRulXVY" target="_blank" rel="noreferrer">https://youtu.be/7pxZhRulXVY</a>- Scientific video: <a href="https://www.youtube.com/watch?v=BY23UXwM1sM" target="_blank" rel="noreferrer">https://www.youtube.com/watch?v=BY23UXwM1sM</a></body></html>

Sepehr Samavi and Prof Angela Schoellig next to robot Jack. Credit: Astrid Eckert / TUM

Researchers at the Technical University of Munich (TUM) have developed a wheeled robot that makes its way through a crowd of people safely and without hesitation. A computer on board predicts the movement of people in the vicinity and how they are likely to react to the robot.

Robot Jack moves like a human

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