Learning Whole-Body Humanoid Locomotion via Motion Generation and Motion Tracking

Zewei Zhang, Kehan Wen, Michael Xu, Junzhe He, Chenhao Li, Takahiro Miki, Clemens Schwarke, Chong Zhang, Xue Bin Peng, Marco Hutter

Keywords: Learning from Human Motions, Whole-Body Humanoid Locomotion, Motion Imitatioin via Reinforcement Learning, Motion Generation

Abstract:

Whole-body humanoid locomotion is challenging
due to high-dimensional control, morphological instability, and
the need for real-time adaptation to various terrains using
onboard perception. Directly applying reinforcement learning
(RL) with reward shaping to humanoid locomotion often leads to
lower-body-dominated behaviors, whereas imitation-based RL can
learn more coordinated whole-body skills but is typically limited
to replaying reference motions without a mechanism to adapt
them online from perception for terrain-aware locomotion. To
address this gap, we propose a whole-body humanoid locomotion
framework that combines skills learned from reference motions
with terrain-aware adaptation. We first train a diffusion model
on retargeted human motions for real-time prediction of terrain-
aware reference motions. Concurrently, we train a whole-body
reference tracker with RL using this motion data. To improve
robustness under imperfectly generated references, we further
fine-tune the tracker with a frozen motion generator in a
closed-loop setting. The resulting system supports directional
goal-reaching control with terrain-aware whole-body adaptation,
and can be deployed on a Unitree G1 humanoid robot with
onboard perception and computation. The hardware experiments
demonstrate successful traversal over boxes, hurdles, stairs, and
mixed terrain combinations. Quantitative results further show the
benefits of incorporating online motion generation and fine-tuning
the motion tracker for improved generalization and robustness.

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Submission Number: 19