安全なヒューマノイド全身追従のためのフィルタ考慮型ファインチューニング
Filter-Aware Fine-Tuning for Safe Humanoid Whole-Body Tracking
ヒューマノイドの全身追従ポリシーと安全フィルタの間に生じる不整合を分析し、フィルタを考慮したファインチューニング手法CoFiTを提案。制約違反時間を大幅に削減し、実機でも安全に動作することを示した。
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著者: Pranit Mohnot, Christian Helten, Daniele Gammelli, Marco Pavone
分類: cs.RO
原文アブストラクト
Safe whole-body motion is essential for deploying humanoid robots in unstructured environments. Modern humanoid control commonly separates reference specification from execution, with a planner, teleoperator, or motion generator providing a reference that a reinforcement-learning policy tracks through dynamically feasible whole-body control. Runtime safety filters, such as control barrier functions (CBFs), offer a promising approach for enforcing newly introduced constraints via interventions on the tracker's outputs. We show, however, that treating the tracking policy and safety filter independently induces fundamental mismatches, as filtering alters both the executed actions and the induced state distribution. We study this policy-filter interface through case studies that isolate dynamics, objective, and information mismatches, highlight their root causes, and use these insights to develop CoFiT (Constrained Filter-aware Tuning), a filter-aware fine-tuning method for pretrained trackers. Across diverse constraint scenes, CoFiT reduces violation time relative to filter-only training by 91% on TWIST2 and 21% on SONIC, while requiring smaller safety filter corrections. On Unitree G1 hardware, CoFiT reduces violation time by 83% for TWIST2 and completes every trial without operator intervention, whereas 50% of baseline trials require an operator stop. Together, these results provide actionable insights into policy-filter interactions and establish design principles for integrating learned trackers with runtime safety filters.