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Learn the differences between motion profiles! 💨 A real demonstration of engineering skills. In each robotic arm, in each legged robot, you will discover actuators that make the robot move! Let's compare two motion profiles: rectangular and trapezoid. - A rectangular motion profile is like a car with abrupt...

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Lukas Ziegler

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112,250 Aufrufe • vor 6 Monaten •via X (Twitter)

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Turning video into humanoid robot motion! 🤳🏼 Training humanoid robots needs huge amounts of motion data, but real-world capture doesn’t scale. Mocap is expensive, dangerous edge cases are rare, and you can’t ask humans to repeatedly fall or crash. Video2Robot tackles this by converting videos into physics-grounded humanoid simulations. Motion is generated to respect balance, inertia, ground contact, and joint limits, then directly retargeted to robot simulators. One prompt can generate a full humanoid motion sequence, including multi-agent interactions and failure cases like falls or collisions, scenarios that are hard or impossible to capture safely in the real world. The pipeline is model-agnostic and works with existing video generators, making it a practical way to scale data for robots. If robots are going to operate in the real world, they need to be trained on the failures too, not just the perfect demos. Here's the GitHub: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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A toilet-cleaning robot from Reflex Robotics. The best part is when the robot dries its hands... That might be the future of robots doing chores for us. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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A toilet-cleaning robot from Reflex Robotics. The best part is when the robot dries its hands... That might be the future of robots doing chores for us. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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The robot is used to inspect the walls of ships. 🚢 The Magnet Crawler introduced by the University of Bremen is a lightweight and compact climbing robot for magnetic surfaces. The robot has two magnetic wheels driven by geared motors and an elastic tail to improve stability. In the front part of the robot, there is a miniature wireless camera. The robot is remotely teleoperated & the video signal from the camera is displayed to the operator on a portable screen. Let's wait and perhaps the robot will become more autonomous over time. Big fan of robots that make the world a safer place for humans. ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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me: wants to become a master of disaster in electronics and microprocessors son of my mother's friend: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →
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me: wants to become a master of disaster in electronics and microprocessors son of my mother's friend: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Tired of teleoperation? One human video → 1,000s of robot demos. (📍GitHub ) Scaling Robot Data Without Dynamics Simulation or Robot Hardware Real2Render2Real (R2R2R) is a new way to scale robot data without physics simulation or hardware. You take a phone scan + a single monocular human demo. It tracks the motion, renders photorealistic scenes, and generates diverse, robot-agnostic trajectories ready for training. > No teleop, no sim, no robot, just a phone and a video > Train VLA models and diffusion policies directly on the output > Supports multiple robot embodiments with kinematic consistency > 1000s of demos in 1/27 the time of real-world collection Thank you, Max Fu, for sharing!! Project: Paper: Code coming soon: It shows that with the right pipeline, you can scale robot learning data without touching a robot. One of the most interesting directions in scalable robotics today. —— Weekly robotics and AI insights. Subscribe free:

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A versatile robot helping out airports! 🛩️ Meet a two-wheeled robot from Fraunhofer IML! 🦿 This balancing robot with gripping arms successfully completed its initial practical tests at the cargo terminal of Munich Airport a while ago. It transported goods, supported loading and unloading, and assisted with physically demanding logistics tasks. This robot can carry loads of up to 100 kg and reach speeds of around 60 km/h. That’s far beyond what most AMRs can handle, yet it still has arms and a form of mobility that lets it work in spaces designed for people. P.S. What other applications would you use this robot for?! 🦾 ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Is Santiago Bernabéu one of the biggest robots?🤯 Its retractable pitch is basically a massive AS/RS system hidden under the field. The engineering numbers are wild: → Six segments, each ~1,500 tons → Perfectly synchronized drives → Millimeter-accurate positioning inside the underground hypogeum How cool is that! Did you see a bigger robot!? ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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This robot can juggle! 🤹🏼‍♀️ A great engineering showcase as a part of a master's thesis at the Brno University of Technology! CableEndy is a cable-driven parallel robot project at the B&R Industrial Automation Brno office. The robot was created most probably to make all professional Kendama players feel insecure :) Guess the year btw! ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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Robots might learn better from video than from language! 📼 Most Vision-Language-Action (VLA) models learn what to do from text, but still struggle with how things move in the real world. That makes them data-hungry and slow to train. mimic video takes a different route. Instead of grounding robot control in text, it grounds it in video, using large pre-trained video models that already capture physical motion and dynamics. The idea is straightforward: let the video model handle “what will happen next,” and let a smaller control model focus only on turning that visual plan into robot actions. The result is big gains in practice. Robots trained this way need 10× less data, converge twice as fast, and perform better on both simulated benchmarks and real bimanual manipulation tasks. If robots can “imagine” motion using video, control becomes a much simpler problem. Shoutout to Jonas Pai, Liam Achenbach, Oier Mees, Elvis Nava and the rest of the team! Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →

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