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HuB is a unified framework that enables humanoids to execute complex balancing tasks. It integrates reference motion refinement, balance-aware policy learning, and sim-to-real robustness training. Project page:

The Humanoid Hub

86,183 subscribers

53,251 次观看 • 1 年前 •via X (Twitter)

健康养生科学技术教育

Anya Rossi• Live Now

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9 条评论

Moytoy 的头像

Moytoy1 年前

Don't hurt it...teach it kindness and everything will be just fine. Right? @sierracatalina1

RTTS 的头像

RTTS1 年前

Testing Salesforce presents unique challenges due to its complexity, scalability and customizability. RTTS can plan, design & automate a successful testing process for you.

Miguel Guau! 的头像

Miguel Guau!1 年前

¡Ese control es puro nivel pro! Luchar contra el gap sim-to-real es como pasar el jefe final en robótica. 🤖 Ojalá suelten algo open-source pronto.

Christopher Cook 的头像

Christopher Cook1 年前

Oof.

Paul 的头像

Paul1 年前

Insane progress.

Ellis Leonidas 的头像

Ellis Leonidas1 年前

This framework potentially enhances robot stability significantly.

Crypto Pioneer 的头像

Crypto Pioneer1 年前

This is a fascinating development in robotic motion control and balance enhancement.

df 的头像

df1 年前

What is happening here??? This video and the dancing one from Tesla which is currently trending are showing an incredible progress in motorics. The high torque and precise, low-latency control of these motors is absolutely insane.

Adam Dorfx 的头像

Adam Dorfx1 年前

Wow

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Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:

Amazon is training humanoids to move boxes. Makes sense! OmniRetarget is a data generation engine that enables complex loco-manipulation for humanoids. It uses offline retargeting from human MoCap datasets and augments data from single demos to produce 8 hours of trajectories that train RL policies in simulation, transferred zero-shot to real robots. Amazon FAR is the primary affiliation for the paper:

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🚨 Without Any Motion Priors, how to make humanoids do versatile parkour jumping🦘, clapping dance🤸, cliff traversal🧗, and box pick-and-move📦 with a unified RL framework? Introduce WoCoCo: 🧗 Whole-body humanoid Control with sequential Contacts 🎯Unified designs for minimal tuning across tasks 🤖Generalize to various high-DoF robots Website:

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RoboPapers

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🤖 Ever dreamed of controlling a humanoid robot to perform complex, long-horizon tasks — using just a single Vision Pro? 🎉 Meet CLONE: a holistic, closed-loop, whole-body teleoperation system for long-horizon humanoid control! 🏃‍♂️🧍 CLONE enables rich and coordinated interactive tasks: 🥊 boxing 🏓 table tennis 🤲 object pickup 📦 room arrangement 🤝 handover … and more! 🌀 Our closed-loop error correction powered by LiDAR odometry ensures precision, while motion-captured demonstrations supercharge policy learning — unlocking the full potential of the G1 robot. 🎥 It’s hard to squeeze the magic into 1 minute — check out the full video demo and project page here: 🔗

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66,556 次观看 • 1 年前