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🕸️ Introducing SPIDER — Scalable Physics-Informed Dexterous Retargeting! A dynamically feasible, cross-embodiment retargeting framework for BOTH humanoids 🤖 and dexterous hands ✋. From human motion → sim → real robots, at scale. 🔗 Website: 🧵 1/n
66,077 次观看 • 11 个月前 •via X (Twitter)
21 条评论

🧵 2/n Retargeting human motion is hard: embodiment gap, missing forces, noisy data. IK fails ❌, RL is costly ❌. SPIDER converts kinematic human demos into dynamically feasible robot trajectories using physics-informed sampling + virtual contact guidance. 👇 Pipeline

🧵 3/n Because SPIDER is grounded with physics, the generated motions are directly executable on real hardware—no tuning. Zero robot demos. No RL training. Just human → physics → real robot.

🧵 4/n SPIDER supports diverse embodiments: 🤖 humanoid + ✋ dexterous hand 📦 2.4M robot-feasible frames 🤖 9 embodiments 🧊 103 objects ⏱️ 800 hrs of data

🧵 5/n SPIDER is also a data engine: We vary object size, states, and scenes to produce massive, physics-valid training data.

🧵 6/n For more details, please refer to: 🔗 Website: 📄 Paper: Huge thanks to the team at FAIR + CMU! We hope SPIDER pushes scalable, physics-grounded retargeting forward 🕷️🤖

jc-bao🦾

@ChongZitaZhang Thanks Chong!

Amazing work. Congrats @ChaoyiPan!

Thanks, Jingyun!

@pablovelagomez1 👀

Really impressive! When kinematic-based retargeter fails to preserve necessary physical information(like contact during interaction) and neural-based retargeter is hard to directly train, such physics-aware retarger becomes as the best option. Looking forward to the release!

Thanks Weishuai!

Very impressive work, congrats jc-bao!

Thank you Wenli!

Congratulations!

Dynamic feasible retargeting 🔥congrats jc-bao! 🦾!

👏👏👏🙏🙏

Impressive work! Hopefully one day we can retarget robot-spider motions using SPIDER!

haha, we will see ;)

...this doesn't seem to work worth shit, why are you all spamming it so hard?

Really cool work!! 👏👏 Since you perform dynamics-level retargeting using sampling-based MPC that generates actions, I wonder if instead of "RL policy training" (Sec 4.2) you could just distill MPC into a policy using imitation learning / DAgger?
