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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 просмотров • 9 месяцев назад •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.

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.

The Humanoid Hub

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Humanoids need a single, generalist control policy for all of their physical tasks, not a new one for every new chore or demo. A policy for walking can't dance. A policy for dancing can't support mowing the lawn. We need to scale up humanoid control for diverse behaviors, just like we did with language and video models. Watch this humanoid, supported by a single control policy, to start its day, mow the lawn, jog, dance, and even hold the elevator for a colleague. This is the "System 1" for humanoids—a robust, natural humanoid controller that will support general-purpose autonomy. Project Page: #Robotics #AI #NVIDIA #SONIC #FoundationModel 🤖🔥

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Helix is now learning directly from human video data We have already trained on data collected in the real world, including Brookfield residential units To our knowledge, this is the first instance of a humanoid robot learning navigation end-to-end using only human video

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🤔 Ever wondered if simulation-based animation/avatar learnings can be applied to real humanoid in real-time? 🤖 Introducing H2O (Human2HumanOid): - 🧠 An RL-based human-to-humanoid real-time whole-body teleoperation framework - 💃 Scalable retargeting and training using large human motion dataset - 🎥 With just an RGB camera, everyone can teleoperate a full-sized humanoid to perform actions like pick and place, walking, kicking, boxing, etc - 💡Unleash the potential of humanoids with human cognitive skills and adaptability 🔗: 📄: 🎬: H2O proposes: - A scalable retargeting framework for obtaining large-scale humanoid motion dataset, intelligently filtering out infeasible motion for the humanoid embodiment. - We train a full-body motion imitator (similar to PHC) and deploy to the real world zero-shot. - Using this framework, we enable real-time teleoperation of a humanoid via a human operator and webcam, performing skills such as pick and place, kicking, walking strollers, etc. Team: Tairan He Zhengyi “Zen” Luo Wenli Xiao @ChongZitaZhang Kris Kitani Changliu Liu Guanya Shi

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RoboPapers

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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

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.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 просмотров • 22 дней назад