[2603.19305] PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking

Computer Science > Robotics arXiv:2603.19305 (cs) [Submitted on 13 Mar 2026] Title:PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking Authors:Jiacheng Bao, Haoran Yang, Yucheng Xin, Junhong Liu, Yuecheng Xu, Han Liang, Pengfei Han, Xiaoguang Ma, Dong Wang, Bin Zhao View a PDF of the paper titled PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking, by Jiacheng Bao and 9 other authors View PDF 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 ...