PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking

TL;DR

PhyGile introduces a physics-guided motion generation framework for humanoid robots, enabling agile, stable, and realistic whole-body motions directly from text prompts, surpassing prior methods in complexity and stability.

Contribution

It presents a novel physics-prefix-guided motion generation method that directly produces robot-native motions, reducing artifacts and improving execution fidelity in humanoid control.

Findings

Enables stable tracking of complex, agile motions on real robots.

Reduces inference-time retargeting artifacts and discrepancies.

Outperforms prior methods in executing difficult motions.

Abstract

Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation models are predominantly trained on captured human motion datasets, whose priors assume human biomechanics, actuation, mass distribution, and contact strategies. When such motions are directly retargeted to humanoid robots, the resulting trajectories may satisfy geometric constraints (e.g., joint limits and pose continuity) and appear kinematically reasonable. However, they frequently violate the physical feasibility required for real-world execution. To address these issues, we present PhyGile, a unified framework that closes the loop between robot-native motion generation and General Motion Tracking (GMT). PhyGile performs physics-prefix-guided robot-native motion generation at inference time, directly generating robot-native motions in a 262-dimensional skeletal space with physics-guided prefixes, thereby eliminating inference-time retargeting artifacts and reducing generation-execution discrepancies. Before physics-prefix adaptation, we train the GMT controller with a curriculum-based mixture-of-experts scheme, followed by post-training on unlabeled motion data to improve robustness over large-scale robot motions. During physics-prefix adaptation, the GMT controller is further fine-tuned with generated objectives under physics-derived prefixes, enabling agile and stable execution of complex motions on real robots. Extensive offline and real-robot experiments demonstrate that PhyGile expands the frontier of text-driven humanoid control, enabling stable tracking of agile, highly difficult whole-body motions that go well beyond walking and low-dynamic motions typically achieved by prior methods.