確率的時間論理仕様を満たす共有制御の自律プロトコル合成
Synthesis of Provably Correct Autonomy Protocols for Shared Control
人間と自律プロトコルの指令を統合する共有制御系をMDPでモデル化し、逆強化学習で人間行動を抽象化した上で、確率的時間論理の安全・性能仕様を満たす自律プロトコルを準凸計画で合成する手法を提案した。
著者: Murat Cubuktepe, Nils Jansen, Mohammed Alsiekh, Ufuk Topcu
分類: cs.RO, cs.AI, cs.LG, math.OC
原文アブストラクト
We synthesize shared control protocols subject to probabilistic temporal logic specifications. More specifically, we develop a framework in which a human and an autonomy protocol can issue commands to carry out a certain task. We blend these commands into a joint input to a robot. We model the interaction between the human and the robot as a Markov decision process (MDP) that represents the shared control scenario. Using inverse reinforcement learning, we obtain an abstraction of the human's behavior and decisions. We use randomized strategies to account for randomness in human's decisions, caused by factors such as complexity of the task specifications or imperfect interfaces. We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic. Additionally, the resulting strategies generate behavior as similar to the behavior induced by the human's commands as possible. We solve the underlying problem efficiently using quasiconvex programming. Case studies involving autonomous wheelchair navigation and unmanned aerial vehicle mission planning showcase the applicability of our approach.
