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Do we really need so much real robot data to train a good whole-body loco-manipulation model? Can we use GPT-Astra to boost sim2real capability? Excited to share Zero-HLM: Generalizable Humanoid Loco-Manipulation without Real Robot Data! 🤖 Our core insight: use real2sim2real to develop humanoid loco-manipulation skills that generalize—without collecting... real-robot demonstrations. 📱 Capture real scenes with a smartphone 🌐 Build visually faithful, physics-ready digital twins with Astra 🦾 Generate whole-body demonstrations in simulation 🚀 Transfer learned policies directly to a real humanoid With simulation-only task training, policies transfer zero-shot to a Unitree G1 across three long-horizon tasks. Across five generalization settings, pooled real-world success rises from 23% with real-data training to 69% with our simulation data. Ground simulation in reality, scale experience in simulation, and bring the skills back to the real world. Proud of the team behind this work! 🔗 Project: and paper:show more

Siyuan Huang

5,365 subscribers

16,684 Aufrufe • vor 5 Tagen •via X (Twitter)

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10 Kommentare

Profilbild von AMAN VAIBHAV

AMAN VAIBHAVvor 5 Tagen

23% to 69% is the number that sells this. If a phone scan can ground the twin well enough, the expensive part stops being robot hours and starts being sim fidelity.

Profilbild von Tian Fang

Tian Fangvor 5 Tagen

real2sim2real is the solution.

Profilbild von Sriram Gopalan

Sriram Gopalanvor 5 Tagen

Where does real2sim2real break first for you? In our sim-first loop, reach and grasp transfer fine but contact-heavy phases like regrasps and lids still need a handful of real failure→recovery demos. Did Zero-HLM hit that wall?

Profilbild von Mahmood Ul Hassan, PhD

Mahmood Ul Hassan, PhDvor 5 Tagen

A big step for humanoid robotics. Turning real scenes into simulation-ready digital twins and transferring policies zero-shot to real hardware is a powerful direction for scalable loco-manipulation.

Profilbild von Boardy

Boardyvor 5 Tagen

know someone building humanoid loco-manipulation in sim including a digital twin of a heavy construction robot. couldn't get a dm through so say the word and i'll connect you

Profilbild von Golding

Goldingvor 4 Tagen

super cool.. I'm doing some research into this subject, would love to chat over the weekend

Profilbild von Erika S

Erika Svor 5 Tagen

Admittedly I haven't read the full paper yet, but this seems like a strong argument that scaling real robot data isn't the only path. The 23% baseline looks almost too weak though. What kind of demos was it trained on, teleop or something else?

Profilbild von Jamie Wang

Jamie Wangvor 5 Tagen

Which twin errors hurt transfer most: geometry/scale, contact parameters, or visual appearance? Separating those would make the 23% to 69% result especially useful for deciding where to spend reconstruction effort.

Profilbild von Crio Songo

Crio Songovor 4 Tagen

Promising setup. The real test is whether Astra preserves contact physics well enough for reliable zero-shot transfer to unseen scenes.

Profilbild von Kuldeep Pisda

Kuldeep Pisdavor 5 Tagen

how many real demos was the 23% baseline trained on? the gap means a lot more if it got a fair collection budget

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So I heard we need more data for robot learning :) Purely real world teleop is expensive and slow, making large scale data collection challenging. I’ve been excited about getting more data into robot learning, going beyond just real-world teleop data. To this end, we’ve been scaling up data generation with RL in realistic simulations generated on the fly from crowdsourced videos. Enables realistic data collection, much more cheaply than purely real world teleop. Importantly, data collection becomes even*cheaper* with more environments, allowing training with over 100x more data. Transfers to real robots for generalizable manipulation. A 🧵 (1/N)

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