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arXiv · 2606.25706

Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots

Yumeng Liu·Dongqi Wang·Jiyu Yu·Yijun Fan·Rong Xiong·Yue Wang
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Abstract

High-level humanoid planners often output sparse task-space, low-rate trajectories, whereas whole-body controllers run at high frequency. This creates temporal asynchrony between the planning and execution, and structural incompleteness for full-body control. We propose an asynchronous upper body task-space tracking framework for humanoids. A student policy is initialized by teacher-student distillation, conditioned on the full cached future trajectory and an execution-time index, and trained with a sliding-window global reward to reduce frame drift without explicit frame estimation. For task-specific post-training, an MPC module completes sparse references into floating-base and upper-body guidance, while action- and FK level self-guidance constrain policy drift. Simulation and Unitree G1 hardware experiments show improved tracking under low update rates, stronger performance than synchronous and decoupled baselines, and safer adaptation to out-of-distribution motions.

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Yumeng Liu, Dongqi Wang, Jiyu Yu, Yijun Fan, Rong Xiong, Yue Wang. 2026-06-24. Learning Asynchronous Upper-body Task-space Trajectory Tracking Policy for Humanoid Robots. https://arxiv.org/abs/2606.25706

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