Latest Caltech Robotics Research Developments

A single robot that can drive like a car, stand upright to get a better view, crawl over tricky terrain, and even take off like a drone - all by adjusting the same four “limbs.” That’s what the M4 Morphobot from Caltech accomplishes. Each wheel can swivel and fold into different positions: as standard wheels for rolling, as “legs” to step over uneven ground, or as propellers for flight. In doing so, this machine sidesteps the limitations that often come with single-purpose designs. How does it work? The M4 carries sensors and an onboard AI processor (NVIDIA Jetson Nano) that help it monitor its surroundings and plan routes in real time. For instance, it uses SLAM (Simultaneous Localization and Mapping) to create a map of the area on the fly, then relies on path-planning algorithms (like A*) to pick the best way forward. If it meets a gap or obstacle that rolling wheels can’t handle, it can switch modes - standing up to get a better look or converting into a drone to fly over the blockage. In real-world situations like search-and-rescue, one type of movement isn’t always enough. Think about collapsed buildings, rugged wilderness, or areas struck by natural disasters. A robot with such adaptability could roll quickly across clear ground, crawl under rubble, and then lift off to reach otherwise inaccessible places - all without specialized add-ons or multiple machines. For space exploration, a “rover-drone hybrid” could tackle rocky planetary surfaces, then take flight to jump over craters or cliffs. NASA’s interest in multi-modal designs hints at a future where one shape-shifting robot might replace several single-mode explorers. What do you think about the future of multi-modal robots with the power of AI? #innovation #technoloy #future #management #startups

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!

This is a #robot that walks, flies, skateboards, slacklines, and might do much more one day. With sensors at 1000 times per second & controller being recomputed at 200 times per second(signal being sent to propellors and the leg joints). 200 times it is adjusting to do what it is doing to maintain balance! That’s very interesting. Presenting the design and control of a multimodal locomotion robotic platform called LEONARDO, which bridges the gap between two different locomotion regimes of flying and walking using synchronized control of distributed electric thrusters and a pair of multijoint legs. By combining two distinct locomotion mechanisms, LEONARDO achieves complex maneuvers that require delicate balancing, such as walking on a slackline and skateboarding, which are challenging for existing bipedal robots. LEONARDO also demonstrates agile walking motions, interlaced with flying maneuvers to overcome obstacles using synchronized control of propellers and leg joints. The mechanical design and synchronized control strategy achieve a unique multimodal locomotion capability that could potentially enable robotic missions and operations that would be difficult for single-modal locomotion robots. #research #paper: https://lnkd.in/dwDXRuPf #author: Kyunam Kim, Patrick Spieler, Elena-Sorina Lupu, Alireza Ramezani, Soon-Jo Chung Aerospace Robotics and Control Lab at Caltech. #robotics #drones #quadcopter #technology #future

The new robot, dubbed M4 (for Multi-Modal Mobility Morphobot) can roll on four wheels, turn its wheels into rotors and fly, stand on two wheels like a meerkat to peer over obstacles, "walk" by using its wheels like feet, use two rotors to help it roll up steep slopes on two wheels, tumble, and more. A robot with such a broad set of capabilities would have applications ranging from the transport of injured people to a hospital to the exploration of other planets, says Mory Gharib (PhD '83), the Hans W. Liepmann Professor of Aeronautics and Bioinspired Engineering and director of Caltech's Center for Autonomous Systems and Technologies (CAST), where the robot was developed.