Advances in Robotics Driven by MIT Research

Massachusetts Institute of Technology researchers just dropped something wild; a system that lets robots learn how to control themselves just by watching their own movements with a camera. No fancy sensors. No hand-coded models. Just vision. Think about that for a second. Right now, most robots rely on precise digital models to function - like a blueprint telling them exactly how their joints should bend, how much force to apply, etc. But what if the robot could just... figure it out by experimenting, like a baby flailing its arms until it learns to grab things? That’s what Neural Jacobian Fields (NJF) does. It lets a robot wiggle around randomly, observe itself through a camera, and build its own internal "sense" of how its body responds to commands. The implications? 1) Cheaper, more adaptable robots - No need for expensive embedded sensors or rigid designs. 2) Soft robotics gets real - Ever tried to model a squishy, deformable robot? It’s a nightmare. Now, they can just learn their own physics. 3) Robots that teach themselves - instead of painstakingly programming every movement, we could just show them what to do and let them work out the "how." The demo videos are mind-blowing; a pneumatic hand with zero sensors learning to pinch objects, a 3D-printed arm scribbling with a pencil, all controlled purely by vision. But here’s the kicker: What if this is how all robots learn in the future? No more pre-loaded models. Just point a camera, let them experiment, and they’ll develop their own "muscle memory." Sure, there are still limitations (like needing multiple cameras for training), but the direction is huge. This could finally make robotics flexible enough for messy, real-world tasks - agriculture, construction, even disaster response. #AI #MachineLearning #Innovation #ArtificialIntelligence #SoftRobotics #ComputerVision #Industry40 #DisruptiveTech #MIT #Engineering #MITCSAIL #RoboticsResearch #MachineLearning #DeepLearning

At MIT, a GenAI model just redesigned a jumping robot that outperformed its human-built version: +41% jump height -84% falls And a curved design no human had even considered. The researchers had tried to make the links thinner. The AI made them rounder. More elastic. Better energy storage. Same materials. Entirely different physics. The AI didn’t “copy” anything. It created something we hadn’t imagined, by simulating structure, behavior, and outcomes all at once. The next design breakthrough might not come from someone thinking harder. It will come from a model collaborating with a human… jumping sideways. MIT CSAIL is already hinting at natural-language prompts to generate physical robots (“one that picks up a mug” etc.). At that point, you’re not “designing” anymore; you’re describing intent, and letting the system work backwards from there. That’s a paradigm shift. From engineer-as-architect… to engineer-as-curator. From "what should I build?" to "what are the properties I need?" Curious what this means for enterprise design, R&D, and the future of product development? So am I. More here: https://lnkd.in/exZPU6Xt #AIEngineering #AgenticAI #Robotics #GenAI

MIT researchers have created a new imaging method called mmNorm that lets robots see inside closed boxes and behind walls—using signals similar to Wi-Fi. The system uses millimeter wave (mmWave) signals to scan through materials like cardboard, plastic, and walls. These signals bounce off hidden objects and are turned into detailed 3D models by an algorithm. In tests, mmNorm achieved 96% accuracy, clearly detecting items like mugs, power drills, and silverware—even if they were hidden or had complex shapes. This breakthrough could let warehouse robots check for damage inside packages—like a broken mug handle—without opening the box, making quality control faster and smarter. #MIT #Robotics #AI #ImagingTech #XRayVision #3DImaging #RobotVision

How Can Gen AI Revolutionize Robot Learning? MIT’s Computer Science and AI Lab (CSAIL) has unveiled a promising breakthrough in robotics training—LucidSim, a system powered by generative AI that could help robots learn complex tasks more efficiently. Traditionally, robots have struggled with a lack of training data—but LucidSim taps into the power of AI-generated imagery to create diverse, realistic simulations. By combining text-to-image models, physics simulations, and auto-generated prompts, LucidSim can rapidly produce large amounts of training data for robots—whether it’s teaching them to navigate parkour-style obstacles or chase a soccer ball. This system outperforms traditional methods like domain randomization and even human expert imitation in many tasks. Key takeaways: - Generative AI is being used to scale up data generation for robotics training, overcoming the industry’s current data limitations. - LucidSim has shown strong potential for improving robot performance and pushing humanoid robots toward new levels of capability. - Researchers aim to improve robot learning and general intelligence to help robots handle more real-world challenges. With robots continuing to grow in sophistication, this innovative approach could mark a significant step toward more capable, intelligent machines in the future!

MIT Just Cracked the Code. 19 Neurons Now Pilot Drones Better Than 100,000-Parameter Models MIT's "liquid neural networks" sound like sci-fi. They're not. Just 19 neurons inspired by a worm's brain now outperform massive AI models in drone navigation. 10x less power. 50% fewer tracking errors. Running on a Raspberry Pi. The breakthrough. These networks adapt in real-time. No retraining. They learn causality, not correlations. Traditional AI sees a shadow and crashes. Liquid networks understand shadows move with the sun. They adjust. Real-world tests prove it. • Navigate through smoke and wind gusts • Handle seasonal changes (summer forest → winter) • Switch tasks mid-flight without updates • Run on battery-powered edge devices Why this matters for defense. Current military drones need constant updates from Ukraine's battlefield. Takes 24-48 hours minimum. Liquid networks adapt in seconds. Three immediate applications. Search-and-rescue in fire zones. Drones weave through smoke that blinds traditional AI. No GPS needed. Logistics in contested airspace. Packages delivered despite jamming. Networks learn new routes instantly. Agricultural monitoring. The same drone handles open fields and dense orchards. Adapts to weather without reprogramming. The kicker. MIT tested this against L1 adaptive control systems. 81% improvement in trajectory tracking. With neural networks, you could sketch on a napkin. For contractors. Forget massive GPU clusters. These run on $35 hardware. Battery life measured in hours, not minutes. We've been building AI backwards. Bigger isn't better. Smarter is. Nature figured this out with 302 neurons. MIT just proved it scales. Your move. While competitors chase trillion-parameter models, the future flies on 19 neurons.

What if technology did not just replace what was lost but actually extended what we are capable of? At MIT’s Media Lab, researchers are reimagining prosthetics by connecting them directly to the nervous system. Research assistant Everett Lawson, who went through an experimental amputation procedure, has designed and controlled his own bionic leg. Muscle signals from his body translate into robotic movement in real time. This breakthrough is more than a medical advancement. It is a powerful example of how biomechatronics can move beyond restoring mobility to enhancing human capability itself. The line between human and robot is no longer science fiction. It is being drawn in research labs today.

The innovation around #AI coming out of Massachusetts Institute of Technology's Department of Electrical Engineering and Computer Science continues to fascinate. This latest story by @Jennifer Chu explains their work to connect robot motion data with the “common sense knowledge” of large language models (#LLMs) to enable self-correction and improved task performance. The development, which enables robots to "physically adjust to disruptions within a subtask so that the robot can move on without having to go back and start a task from scratch...," could have far-reaching impact across a range of industries. Again, fascinating. https://lnkd.in/e96_eh-y Hitachi Digital Hitachi Digital Services Frank Antonysamy

Have you ever imagined a robot so small and flexible that it could journey through the intricate blood vessels of the human brain? Researchers at MIT have developed an ultra-thin, flexible robotic thread that can be magnetically guided to remove dangerous blood clots. Stroke is one of the leading causes of death and disability worldwide. Quick treatment is critical; the sooner a blockage can be addressed, the better the chances of survival and recovery. The interventions within the first hour after a stroke occurs, are crucial for minimizing brain damage. 𝐇𝐨𝐰 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐑𝐨𝐛𝐨𝐭𝐢𝐜 𝐓𝐡𝐫𝐞𝐚𝐝 𝐖𝐨𝐫𝐤? - Material Composition: The thread is made from nitinol, a nickel-titanium alloy known for its flexibility and ability to return to its original shape. This makes it ideal for navigating the brain's complex vascular pathways. - Hydrogel Coating: The thread is coated with a hydrogel, a slippery, biocompatible material that allows it to glide smoothly through blood vessels without causing damage or friction. - Magnetic Guidance: Doctors can steer the thread using external magnets, precisely directing it to the site of a clot. - Treatment Delivery: Once at the blockage, the thread could deliver clot-dissolving medications or even use laser technology to break down the clot. 𝐏𝐨𝐭𝐞𝐧𝐭𝐢𝐚𝐥 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬: - Minimally Invasive: Reduces the need for open surgery, lowering risks associated with traditional surgical procedures. - Faster Response Times: Enables quicker intervention during the critical moments after a stroke, improving patient outcomes. How do you see technologies like this shaping the future of healthcare? #innovation #technology #future #management #startups

Well I guess robotic programmers are out of a job now....MIT Just Cracked the Code on Robot Training What if robots could learn to control themselves in just a few hours using nothing but a single camera? No expensive sensors, no weeks of programming. MIT's breakthrough, Neural Jacobian Fields (NJF) system does exactly that - teaching robots to understand their bodies purely through vision. The AI watches 2-3 hours of video footage of a robot making random movements, then builds a complete understanding of how the robot's controls affect its motion. That's it. Here are the facts... - Training time: Days → Hours - Cost: Eliminates expensive sensors - Flexibility: Works on ANY robot type - Accessibility: Makes robotics affordable for more businesses The real applications already emerging: • Healthcare: Precise medical procedures • Manufacturing: Streamlined assembly • Logistics: Smarter warehouse automation • Space: Where traditional sensors fail As MIT researcher Sizhe Lester Li puts it: "Think about how you learn to control your fingers: you wiggle, you observe, you adapt. That's what our system does." This isn't just a technical upgrade, it's a fundamental shift from programming robots to teaching them. The barriers to robotic adoption are falling fast. This mirrors Tesla's breakthrough with vision-only self-driving, eliminating LiDAR sensors and learning from millions of hours of human driving data. Both represent the same paradigm shift: from expensive, sensor-heavy systems to AI that learns by watching and adapting to infinite scenarios.

Teaching robots to build simulations of themselves allows the robot to detect abnormalities and recover from damage. We naturally visualize and simulate our own movements internally, enhancing mobility, adaptability, and awareness of our environment. Robots have historically been unable to replicate this visualization, relying instead on predefined CAD models and kinematic equations. Free Form Kinematic Self-Model (FFKSM) allows the 𝗿𝗼𝗯𝗼𝘁 𝘁𝗼 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗲 𝗶𝘁𝘀𝗲𝗹𝗳: 1) Robots autonomously learn from their morphology, kinematics, and motor control directly from 𝗯𝗿𝗶𝗲𝗳 𝗿𝗮𝘄 𝘃𝗶𝗱𝗲𝗼 𝗱𝗮𝘁𝗮 -> Like humans observing their reflection in a mirror 2) Robots perform precise 3D motion planning tasks 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗽𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗸𝗶𝗻𝗲𝗺𝗮𝘁𝗶𝗰 𝗲𝗾𝘂𝗮𝘁𝗶𝗼𝗻𝘀 -> Simplifies complex manipulation and navigation tasks 3) Robots 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆 𝗱𝗲𝘁𝗲𝗰𝘁 morphological changes or damage and rapidly recover by retraining with new visual feedback -> Significantly enhances resilience. The model is also 𝗵𝗶𝗴𝗵𝗹𝘆 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁, requiring minimal memory resources of just 333kB, making it broadly applicable for resource constrained robotic systems. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝗮𝗹𝘀𝗼 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝗺𝗼𝗱𝗲𝗹 𝘁𝗼 𝗮𝗰𝗵𝗶𝗲𝘃𝗲 𝘀𝘂𝗰𝗵 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝘀𝗲𝗹𝗳-𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝘂𝘀𝗶𝗻𝗴 𝗼𝗻𝗹𝘆 𝟮𝗗 𝗥𝗚𝗕 𝗶𝗺𝗮𝗴𝗲𝘀, 𝗲𝗹𝗶𝗺𝗶𝗻𝗮𝘁𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗱𝗲𝗽𝘁𝗵-𝗰𝗮𝗺𝗲𝗿𝗮 𝘀𝗲𝘁𝘂𝗽𝘀 𝗮𝗻𝗱 𝗶𝗻𝘁𝗿𝗶𝗰𝗮𝘁𝗲 𝗰𝗮𝗹𝗶𝗯𝗿𝗮𝘁𝗶𝗼𝗻𝘀. I believe the next phase of robotic automation inevitably comes with self-awareness of robots. Self-reflection is a major part of how we as humans improve upon ourselves; as 'general purpose robots' emerge, so would their self-reflection. This enables robots to continuously monitor and update their internal models, thereby refining their performance in real time. This is a huge step towards robot self-awareness! Congratulations to Yuhang Hu, Jiong Lin, and Hod Lipson on this impressive advancement! Paper link: https://lnkd.in/gJ-bkU8N I post the latest and interesting developments in robotics—𝗳𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱!