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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

5,183 subscribers

22,030 görüntüleme • 11 ay önce •via X (Twitter)

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Niraj Pudasaini profil fotoğrafı

Niraj Pudasaini11 ay önce

Great work! Really close to our work :

Winston Gao profil fotoğrafı

Winston Gao11 ay önce

Nice work!

ζ Pedram ζ profil fotoğrafı

ζ Pedram ζ11 ay önce

Love this

bob35ooops profil fotoğrafı

bob35ooops8 ay önce

Amazing! I’m curious—how much data did you use for training, and how was it collected? Do all tasks share a single policy?

SoloTech profil fotoğrafı

SoloTech11 ay önce

COLA is a game-changer for human–humanoid collaboration

Tirth Gada profil fotoğrafı

Tirth Gada11 ay önce

Humanoid clerks soon

Sammy profil fotoğrafı

Sammy11 ay önce

This is super helpful!

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The Humanoid Hub

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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!

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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 ✈️ IROS 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 ✈️ IROS join us to tell us about their method. Watch Episode #80 of RoboPapers, with Chris Paxton and Jiafei Duan, now to learn more!

RoboPapers

30,095 görüntüleme • 4 ay önce