支援ロボティクスにおける安全な学習と適応のための証明可能な制御ポリシー
Certifiably-correct Control Policies for Safe Learning and Adaptation in Assistive Robotics
混合整数二次計画法を用いて学習済みポリシーネットワークを局所的に修正し、形式的安全制約を満たす安全な制御ポリシーを生成するフレームワークを提案し、下肢義肢ロボットに適用した。
著者: Keyvan Majd, Geoffrey Clark, Tanmay Khandait, Siyu Zhou, Sriram Sankaranarayanan, Georgios Fainekos, Heni Ben Amor
分類: cs.RO
原文アブストラクト
Guaranteeing safety in human-centric applications is critical in robot learning as the learned policies may demonstrate unsafe behaviors in formerly unseen scenarios. We present a framework to locally repair an erroneous policy network to satisfy a set of formal safety constraints using Mixed Integer Quadratic Programming (MIQP). Our MIQP formulation explicitly imposes the safety constraints to the learned policy while minimizing the original loss function. The policy network is then verified to be locally safe. We demonstrate the application of our framework to derive safe policies for a robotic lower-leg prosthesis.
