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Excited to introduce COLA: Learning Human–Humanoid Coordination for Collaborative Object Carrying 🤝🤖 COLA makes humanoids truly helpful in human collaboration — capable of carrying objects, pushing carts, or responding to human push commands. It provides a proprioception-only policy for compliant human–humanoid coordination across diverse movement patterns. The core idea... show more

Siyuan Huang

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21,932 views • 10 months ago •via X (Twitter)

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HDMI (HumanoiD iMitation for Interaction) is a framework enabling humanoid robots to learn whole-body object interaction skills from monocular RGB human videos. It extracts and retargets human poses and object trajectories using GVHMR and LocoMujoco, building reference datasets with contact annotations. The data is used to train an RL policy via robot-object co-tracking. HDMI achieved 67 consecutive door traversals.

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The Humanoid Hub

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Boston Dynamics has demonstrated its new Atlas humanoid robot lifting and carrying a 50-pound fridge. The robot uses its whole body to balance and move the heavy object in a natural, human-like way. Before doing it in the real world, Atlas practiced the task for millions of hours in a virtual simulation.

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Looking for comfort is a human instinct. The goal of cycling policy is to make cycling a pleasant experience. To guarantee a comfortable situation, it is imperative to minimize stops and nuisances in the network.

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Haven't been to a conference in a while, really excited to be at #NeurIPS2024! I'll be helping present 4 of our group's recent papers: 1. Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL 2. Distributional Successor Features Enable Zero-Shot Policy Optimization 3. Learning to Cooperate with Humans using Generative Agents 4. Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning Find more details on each paper and where to find us in this thread (1/6)

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Abhishek Gupta

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For robots to be useful, they can’t just dance — they must be able to physically interact with the world around them. Unfortunately, the sorts of motion tracking policies you see performing dancing or martial arts are not really capable of the kind of precise, forceful interaction needed to perform useful interactions with the world. Siheng Zhao and Yanjie Ze join us to talk about ResMimic, their new paper which takes a general-purpose human motion-tracking policy and improves it with a residual policy to reliably interact with objects To learn more, watch Episode #47 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, today!

For robots to be useful, they can’t just dance — they must be able to physically interact with the world around them. Unfortunately, the sorts of motion tracking policies you see performing dancing or martial arts are not really capable of the kind of precise, forceful interaction needed to perform useful interactions with the world. Siheng Zhao and Yanjie Ze join us to talk about ResMimic, their new paper which takes a general-purpose human motion-tracking policy and improves it with a residual policy to reliably interact with objects To learn more, watch Episode #47 of RoboPapers, hosted by Michael Cho - Rbt/Acc and Chris Paxton, today!

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11,399 views • 8 months ago

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

The power of generative models — now embodied in humanoids. Announcing DreamControl –– After a year-long research effort at General Robotics — we present a scalable framework for whole-body humanoid control that fuses diffusion priors with reinforcement learning to unlock real-world scene interaction. Diffusion + RL → natural whole-body skills on real robots. DreamControl enables humanoids to move beyond locomotion demos → performing natural, human-like skills such as –– Picking & lifting objects, Opening drawers & doors, Precise punching, kicking, and jumping, Bimanual manipulation tasks Our key innovation: a diffusion prior over human motion that guides RL, eliminating the need for massive teleoperation datasets, and producing motions that look human while transferring to real hardware. Trained purely in simulation, deployed on the Unitree G1 humanoid, DreamControl policies run in real time, bridging sim-to-real with unprecedented naturalness. We leverage a novel hybrid edge + cloud infrastructure that runs RL-trained policies on the edge backed by powerful AI models running in the cloud This is the next step in General Robotics’ journey toward general-purpose humanoid assistants that interact, adapt, and assist autonomously. Paper: Blog: 1/n

Ashish Kapoor

118,133 views • 11 months ago

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

Sports like tennis are great examples of the sort of dynamic whole-body interaction that’s possible with humanoid robots. But capturing examples of fast, dynamic interactions from humans is really difficult. Enter LATENT, which uses lower-quality human data plus reinforcement learning to teach a robot to play tennis, able to complete back-and-forth volleys at a human level. LATENT has three steps: (1) collecting imperfect human data like a backswing, (2) using these to learn a latent action space, and (3) they train a high-level policy in simulation which can compose these actions and execute tennis skills on a robot. Haofei Lu and Yunrui Lian join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

RoboPapers

30,004 views • 3 months ago

.travis kalanick on why specialized robots beat humanoids for real work: "I'm not anti-humanoid, I'm non-humanoid. There are tasks that are good for humanoids, low scale, high diversity, in human environments, humanoid all the way. But the minute you have a machine doing a high-scale task, we're no longer in humanoid land, we're in specialized robotics land." "There was a moment nine months ago where humanoids were the mega rage of all time. When your hairdresser says he's getting into the humanoid business, we know something's wrong." "If I had to make 1,000 pancakes an hour, I'd never have a humanoid do it, you'd need 100 humanoids going gg-gg-gg. It's like the humanoid Olympics in Beijing, they're doing the marathon and it's cool, but all I could think is, imagine if you put wheels on them. Would you ever put a humanoid in a car to drive it?" "High scale, repetitive tasks do not need to be in human form, and they won't be, because humans are meant to do lots of things at low scale."

.travis kalanick on why specialized robots beat humanoids for real work: "I'm not anti-humanoid, I'm non-humanoid. There are tasks that are good for humanoids, low scale, high diversity, in human environments, humanoid all the way. But the minute you have a machine doing a high-scale task, we're no longer in humanoid land, we're in specialized robotics land." "There was a moment nine months ago where humanoids were the mega rage of all time. When your hairdresser says he's getting into the humanoid business, we know something's wrong." "If I had to make 1,000 pancakes an hour, I'd never have a humanoid do it, you'd need 100 humanoids going gg-gg-gg. It's like the humanoid Olympics in Beijing, they're doing the marathon and it's cool, but all I could think is, imagine if you put wheels on them. Would you ever put a humanoid in a car to drive it?" "High scale, repetitive tasks do not need to be in human form, and they won't be, because humans are meant to do lots of things at low scale."

MTS

52,573 views • 1 month ago