Ep#76: OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control

We’ve seen lots of incredible videos of humanoid robots dancing, doing martial arts, running up walls — but these extreme behaviors are usually from individual, highly specialized policies. But now OmniXtreme shows us how to achieve incredible behaviors that push the limits of humanoid motion, by (1) training a flow-based motion generative model, and (2) doing residual RL post-training to handle complex real-world dynamics.

Yunsheng Wang and Shaohang Zhu join us to talk about their work towards general-purpose high performance humanoid robot control.

Watch Episode #76 of RoboPapers, with Michael Cho and Jiafei Duan, now!

Abstract

High-fidelity motion tracking serves as the ultimate litmus test for generalizable, human-level motor skills. However, current policies often hit a "generality barrier": as motion libraries scale in diversity, tracking fidelity inevitably collapses - especially for real-world deployment of high-dynamic motions. We identify this failure as the result of two compounding factors: the learning bottleneck in scaling multi-motion optimization and the physical executability constraints that arise in real-world actuation. To overcome these challenges, we introduce OmniXtreme, a scalable framework that decouples general motor skill learning from sim-to-real physical skill refinement. Our approach uses a flow-matching policy with high-capacity architectures to scale representation capacity without interference-intensive multi-motion RL optimization, followed by an actuation-aware refinement phase that ensures robust performance on physical hardware. Extensive experiments demonstrate that OmniXtreme maintains high-fidelity tracking across diverse, high-difficulty datasets. On real robots, the unified policy successfully executes multiple extreme motions, effectively breaking the long-standing fidelity-scalability trade-off in high-dynamic humanoid control.

Learn More

Project Page: https://extreme-humanoid.github.io/

Github: https://github.com/Perkins729/OmniXtreme

ArXiV: https://arxiv.org/abs/2602.23843

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