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

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.

Robotics (cs.RO)

arXiv:2606.25706 [cs.RO]

(or arXiv:2606.25706v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.25706 Focus to learn more arXiv-issued DOI via DataCite

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