ハミルトン・ヤコビ到達可能性を用いた安全保証のスケーラブルな学習
Scalable Learning of Safety Guarantees for Autonomous Systems using Hamilton-Jacobi Reachability
分解・ウォームスタート・適応グリッドを組み合わせ、ハミルトン・ヤコビ到達可能性による安全集合の更新を大幅に高速化し、10次元のクアッドコプターでも実用的に学習できるようにした論文。
著者: Sylvia Herbert, Jason J. Choi, Suvansh Sanjeev, Marsalis Gibson, Koushil Sreenath, Claire J. Tomlin
分類: cs.RO, cs.LG, cs.SY, eess.SY
原文アブストラクト
Autonomous systems like aircraft and assistive robots often operate in scenarios where guaranteeing safety is critical. Methods like Hamilton-Jacobi reachability can provide guaranteed safe sets and controllers for such systems. However, often these same scenarios have unknown or uncertain environments, system dynamics, or predictions of other agents. As the system is operating, it may learn new knowledge about these uncertainties and should therefore update its safety analysis accordingly. However, work to learn and update safety analysis is limited to small systems of about two dimensions due to the computational complexity of the analysis. In this paper we synthesize several techniques to speed up computation: decomposition, warm-starting, and adaptive grids. Using this new framework we can update safe sets by one or more orders of magnitude faster than prior work, making this technique practical for many realistic systems. We demonstrate our results on simulated 2D and 10D near-hover quadcopters operating in a windy environment.
