故障しうるアクチュエータ下での最適レジリエント計画モデル
A Model for Optimal Resilient Planning Subject to Fallible Actuators
アクチュエータの故障可能性を考慮し、MDP枠組みで利用駆動型故障と状態行動依存の故障確率をモデル化し、故障時にも目標を達成できる戦略的計画手法を提案した。
著者: Kyle Baldes, Diptanil Chaudhuri, Jason M. O'Kane, Dylan A. Shell
分類: cs.RO, cs.AI
原文アブストラクト
Robots incurring component failures ought to adapt their behavior to best realize still-attainable goals under reduced capacity. We formulate the problem of planning with actuators known a priori to be susceptible to failure within the Markov Decision Processes (MDP) framework. The model captures utilization-driven malfunction and state-action dependent likelihoods of actuator failure in order to enable reasoning about potential impairment and the long-term implications of impoverished future control. This leads to behavior differing qualitatively from plans which ignore failure. As actuators malfunction, there are combinatorially many configurations which can arise. We identify opportunities to save computation through re-use, exploiting the observation that differing configurations yield closely related problems. Our results show how strategic solutions are obtained so robots can respond when failures do occur -- for instance, in prudently scheduling utilization in order to keep critical actuators in reserve.
