SHIELD: 学習ダイナミクスに基づく確率的制御バリア関数によるヒューマノイドの安全確保
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
実機データから確率的な残差ダイナミクスモデルを学習し、制御バリア関数を確率的に適用する安全層を既存の強化学習歩行制御に追加することで、Unitree G1ヒューマノイドの屋内・屋外での障害物回避を実現した。
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2. 先行研究と比べてどこがすごい？
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著者: Lizhi Yang, Blake Werner, Ryan K. Cosner, David Fridovich-Keil, Preston Culbertson, Aaron D. Ames
分類: cs.RO
原文アブストラクト
Robot learning has produced remarkably effective ``black-box'' controllers for complex tasks such as dynamic locomotion on humanoids. Yet ensuring dynamic safety, i.e., constraint satisfaction, remains challenging for such policies. Reinforcement learning (RL) embeds constraints heuristically through reward engineering, and adding or modifying constraints requires retraining. Model-based approaches, like control barrier functions (CBFs), enable runtime constraint specification with formal guarantees but require accurate dynamics models. This paper presents SHIELD, a layered safety framework that bridges this gap by: (1) training a generative, stochastic dynamics residual model using real-world data from hardware rollouts of the nominal controller, capturing system behavior and uncertainties; and (2) adding a safety layer on top of the nominal (learned locomotion) controller that leverages this model via a stochastic discrete-time CBF formulation enforcing safety constraints in probability. The result is a minimally-invasive safety layer that can be added to the existing autonomy stack to give probabilistic guarantees of safety that balance risk and performance. In hardware experiments on an Unitree G1 humanoid, SHIELD enables safe navigation (obstacle avoidance) through varied indoor and outdoor environments using a nominal (unknown) RL controller and onboard perception.
