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See Spot perform dynamic whole-body manipulation. Using a combination of reinforcement learning (RL) and sampling-based control, the robot is able to autonomously drag, roll, and stack tires weighing 15 kg (33 lb), well above its maximum arm lift capacity. Learn more about coordinating locomotion and manipulation processes:

RAI Institute

7,885 subscribers

87,700 görüntüleme • 11 ay önce •via X (Twitter)

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Flesh & Bone VR profil fotoğrafı

Flesh & Bone VR11 ay önce

That leg up was super impressive. You can't teach these non humanoids with mocap.

Олег Сельдюгаев profil fotoğrafı

Олег Сельдюгаев11 ay önce

This video makes it clear that a second hand is necessary for quick and precise loading and unloading operations. The second hand can be simpler than the primary hand.

iRiSh profil fotoğrafı

iRiSh11 ay önce

I hope I live long enough to see the future where these are everywhere. I want one. Everyone wants one

Kwasikot profil fotoğrafı

Kwasikot11 ay önce

The most amazing dog! What an incredible achievement! 😊

BadassRockets profil fotoğrafı

BadassRockets11 ay önce

Two arms with guns Charging kennels Secured borders

Jo profil fotoğrafı

Jo11 ay önce

Sandbag houses is the best use case. Whole houses built one bag at a time with a Spot bot.

Dennis profil fotoğrafı

Dennis11 ay önce

Quadruped with one arm trying to train in novel ways to manipulate things that we already have plenty of human based training data, is rather inneficient.

Sammy profil fotoğrafı

Sammy11 ay önce

It is doing it exceptionally well.

Max's Ghost profil fotoğrafı

Max's Ghost11 ay önce

That's great. Come back when he can mount and balance it.

furtive pygmy (parody) 💨 profil fotoğrafı

furtive pygmy (parody) 💨11 ay önce

tires with ir markers on them are so futuristic, also narrow sidewalls

Mick Russom profil fotoğrafı

Mick Russom11 ay önce

yeah, now tell people about the abysmal battery life.

Marie profil fotoğrafı

Marie11 ay önce

Say NO to Robots! 🤖 Join the ARA.. fight for humanity! 😉

Anto Patrex profil fotoğrafı

Anto Patrex11 ay önce

This is pretty impressive, specially with one arm.

All4000ft profil fotoğrafı

All4000ft11 ay önce

Must have been 2 tired to continue.

Joshua S McConkey profil fotoğrafı

Joshua S McConkey11 ay önce

@XiXiDu That is so far beyond anything else I have seen in robotics. What a huge step forward.

Porters Reserve profil fotoğrafı

Porters Reserve11 ay önce

Great work now bring it to the reserve and let see if it can change the tires on our old tractor.

Ryan Industrialist profil fotoğrafı

Ryan Industrialist11 ay önce

33lb tire drag? Strong 'UPS driver tossing packages' vibes. Jokes aside, this hierarchical control could revolutionize high-speed depalletizing-finally bots that handle shifting pallets like humans.

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Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

Robot policies must be both reliable and highly capable to be useful; the best way to achieve this level of performance is with reinforcement learning. However, for reinforcement learning you are usually stuck between two difficult options: reinforcement in the real world is often risky and expensive, while reinforcement learning in a traditional simulator takes a lot of engineering work and has a persistent sim-to-real gap. What if instead you could train your robot purely in a world model? RISE by Jiazhi Yang et al. uses a compositional world model to predict the future and evaluate progress. This allows for a self-improving pipeline, which learns a world model from real data and then learns how the robot should perform different tasks. This pipeline results in a data-driven way to improve policy performance from real data but without real-world reinforcement learning. Watch Episode #86 of RoboPapers, with Chris Paxton and Jiafei Duan, to learn more!

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A vibrant & independent civil society is essential to a healthy society. But the Kremlin weaponizes "civil society" and pseudo-academics to spread its propaganda in the West. Learn more about #Russia's use of #disinformation & information manipulation:

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NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

NEW OPEN SOURCE ROBOT: A cheap, lightweight, and agile platform for locomotion! Called BRIDGE, it is an open-source humanoid robot platform, hardware plus control system. It is an 88 cm, 12.5 kg humanoid with 21 active degrees of freedom that costs roughly $1,500. Its distinguishing idea is that the body and the controller are designed together through a three-stage data-driven pipeline: - anatomical constraints - kinematic criteria - closed-loop dynamic criteria Hence the morphology itself is optimized to preserve human motion quality when the robot executes. It demonstrates teleoperated locomotion, balance-oriented motion (bending, lunging, extreme balance) and highly dynamic motion including back-flips and expressive sequences. I like that the thesis is designing the body and the controller together, not a controller for a fixed body, which inverts how nearly everyone else works; the robot is usually given, usually a Unitree G1, then train a policy on it. At the end of the day, it is a sub-$2K open-source humanoid that does back-flips, the humanoid analogue of the cheap-arm democratization.

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Today, we're joined by Nikita Rudin, co-founder and CEO of Flexion to discuss the gap between current robotic capabilities and what’s required to deploy fully autonomous robots in the real world. Nikita explains how reinforcement learning and simulation have driven rapid progress in robot locomotion—and why locomotion is still far from “solved.” We dig into the sim2real gap, and how adding visual inputs introduces noise and significantly complicates sim-to-real transfer. We also explore the debate between end-to-end models and modular approaches, and why separating locomotion, planning, and semantics remains a pragmatic approach today. Nikita also introduces the concept of "real-to-sim", which uses real-world data to refine simulation parameters for higher fidelity training, discusses how reinforcement learning, imitation learning, and teleoperation data are combined to train robust policies for both quadruped and humanoid robots, and introduces Flexion's hierarchical approach that utilizes pre-trained Vision-Language Models (VLMs) for high-level task orchestration with Vision-Language-Action (VLA) models and low-level whole-body trackers. Finally, Nikita shares the behind-the-scenes in humanoid robot demos, his take on reinforcement learning in simulation versus the real world, the nuances of reward tuning, and offers practical advice for researchers and practitioners looking to get started in robotics today. 🗒️ For the full list of resources for this episode, visit the show notes page: 📖 CHAPTERS =============================== 00:00 - Introduction 04:07 - Is robot locomotion solved? 06:04 - Sim-to-real gap 08:58 - Adding semantics to policies 09:42 - Modular vs end-to-end architectures 10:29 - Planner model 12:21 - Adapting RL techniques from quadrupeds to humanoids 15:39 - Behind robot demos 18:09 - Humanoid robots in home environments 22:03 - Training approach 23:56 - VLA models 27:59 - Closing the sim-to-real gap 32:55 - Task orchestration using VLMs 36:38 - Tool use 38:10 - Model hierarchy 43:37 - Simulator versus simulation environment 44:57 - Combining imitation learning and reinforcement learning 46:42 - RL in real world versus RL in simulation 52:58 - Reward tuning and value functions in robotics 56:38 - Predictions 1:00:10 - Humanoids, quadropeds, and wheeled platforms 1:02:45 - Advice, recommended robot kits, and community pla

The TWIML AI Podcast

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🌱 How do you control a robot whose body is constantly growing, buckling, and reshaping? 📷 Put 19 cameras on it. Meet PanoVine, the first autonomous vine robot system. We distribute 19 cameras along a 6 meters, 7-DoF soft growing vine robot, giving it whole-body visual feedback of both itself and its surroundings. Using these observations, we train an end-to-end visuomotor policy that autonomously navigates branches, slopes, gaps, turns, and obstacles. 🔗

🌱 How do you control a robot whose body is constantly growing, buckling, and reshaping? 📷 Put 19 cameras on it. Meet PanoVine, the first autonomous vine robot system. We distribute 19 cameras along a 6 meters, 7-DoF soft growing vine robot, giving it whole-body visual feedback of both itself and its surroundings. Using these observations, we train an end-to-end visuomotor policy that autonomously navigates branches, slopes, gaps, turns, and obstacles. 🔗

Xiaomeng Xu✈️IROS

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