人間環境におけるロボットマニピュレーションのための安全保証付き深層強化学習
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
人間とマニピュレータの高速な到達可能性解析によるシールド機構を導入し、強化学習の訓練・運用中にISO準拠の人間安全を保証する手法を提案した。衝突によるエピソード終了を防ぎ、RL性能も向上することを人間のモーションキャプチャデータを用いたシミュレーションで示した。
著者: Jakob Thumm, Matthias Althoff
分類: cs.RO, cs.AI
原文アブストラクト
Deep reinforcement learning (RL) has shown promising results in the motion planning of manipulators. However, no method guarantees the safety of highly dynamic obstacles, such as humans, in RL-based manipulator control. This lack of formal safety assurances prevents the application of RL for manipulators in real-world human environments. Therefore, we propose a shielding mechanism that ensures ISO-verified human safety while training and deploying RL algorithms on manipulators. We utilize a fast reachability analysis of humans and manipulators to guarantee that the manipulator comes to a complete stop before a human is within its range. Our proposed method guarantees safety and significantly improves the RL performance by preventing episode-ending collisions. We demonstrate the performance of our proposed method in simulation using human motion capture data.
