PMG: Parameterized Motion Generator for Human-like Locomotion Control

• URL: http://arxiv.org/abs/2602.12656v1
• Date: Fri, 13 Feb 2026 06:38:04 GMT
• Title: PMG: Parameterized Motion Generator for Human-like Locomotion Control
• Authors: Chenxi Han, Yuheng Min, Zihao Huang, Ao Hong, Hang Liu, Yi Cheng, Houde Liu,
• Abstract summary: We develop a real-time motion generator that produces human-like locomotion in a single integrated system.<n>We show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs.<n>These results establish a practical, experimentally validated pathway toward natural and deployable humanoid control.
• Score: 14.637220434597168
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in data-driven reinforcement learning and motion tracking have substantially improved humanoid locomotion, yet critical practical challenges remain. In particular, while low-level motion tracking and trajectory-following controllers are mature, whole-body reference-guided methods are difficult to adapt to higher-level command interfaces and diverse task contexts: they require large, high-quality datasets, are brittle across speed and pose regimes, and are sensitive to robot-specific calibration. To address these limitations, we propose the Parameterized Motion Generator (PMG), a real-time motion generator grounded in an analysis of human motion structure that synthesizes reference trajectories using only a compact set of parameterized motion data together with High-dimensional control commands. Combined with an imitation-learning pipeline and an optimization-based sim-to-real motor parameter identification module, we validate the complete approach on our humanoid prototype ZERITH Z1 and show that, within a single integrated system, PMG produces natural, human-like locomotion, responds precisely to high-dimensional control inputs-including VR-based teleoperation-and enables efficient, verifiable sim-to-real transfer. Together, these results establish a practical, experimentally validated pathway toward natural and deployable humanoid control.

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