Robotics: Science and Systems XXII

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

Yunshen Wang, Shaohang Zhu, Peiyuan Zhi, Yuhan Li, Jiaxin Li, Yong-Lu Li, Yuchen Xiao, Xingxing Wang, Baoxiong Jia, Siyuan Huang

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, 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 the 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.

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Bibtex:

  
@INPROCEEDINGS{WangY2-RSS-26, 
    AUTHOR    = {Yunshen Wang AND Shaohang Zhu AND Peiyuan Zhi AND Yuhan Li AND Jiaxin Li AND Yong-Lu Li AND Yuchen Xiao AND Xingxing Wang AND Baoxiong Jia AND Siyuan Huang}, 
    TITLE     = {{OmniXtreme: Breaking the Generality Barrier in High-Dynamic Humanoid Control}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2026}, 
    ADDRESS   = {Sydney, Australia}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2026.XXII.031} 
}