ゲームプレイフィルタ：敵対的想像によるロバストなゼロショット安全性
Gameplay Filters: Robust Zero-Shot Safety through Adversarial Imagination
シミュレーションで訓練した安全戦略と最悪ケースを引き起こす仮想敵を継続的に対戦させ、将来の失敗を防ぐ予測型安全フィルタを提案。四足歩行ロボットの全身動力学に適用し、大きな外乱下でもゼロショットで安全を確保できることを実機で示した。
著者: Duy P. Nguyen, Kai-Chieh Hsu, Wenhao Yu, Jie Tan, Jaime F. Fisac
分類: cs.RO, cs.LG
原文アブストラクト
Despite the impressive recent advances in learning-based robot control, ensuring robustness to out-of-distribution conditions remains an open challenge. Safety filters can, in principle, keep arbitrary control policies from incurring catastrophic failures by overriding unsafe actions, but existing solutions for complex (e.g., legged) robot dynamics do not span the full motion envelope and instead rely on local, reduced-order models. These filters tend to overly restrict agility and can still fail when perturbed away from nominal conditions. This paper presents the gameplay filter, a new class of predictive safety filter that continually plays out hypothetical matches between its simulation-trained safety strategy and a virtual adversary co-trained to invoke worst-case events and sim-to-real error, and precludes actions that would cause failures down the line. We demonstrate the scalability and robustness of the approach with a first-of-its-kind full-order safety filter for (36-D) quadrupedal dynamics. Physical experiments on two different quadruped platforms demonstrate the superior zero-shot effectiveness of the gameplay filter under large perturbations such as tugging and unmodeled terrain. Experiment videos and open-source software are available online: https://saferobotics.org/research/gameplay-filter
