制約付き階層型モンテカルロ信念状態プランニング
Constrained Hierarchical Monte Carlo Belief-State Planning
制約付き部分観測マルコフ決定過程(CPOMDP)において、階層的なオプション分解と信念木探索を組み合わせ、大規模連続ロボット問題でも安全制約を満たすオンラインプランニング手法COBeTSを提案した。
著者: Arec Jamgochian, Hugo Buurmeijer, Kyle H. Wray, Anthony Corso, Mykel J. Kochenderfer
分類: cs.AI, cs.RO
原文アブストラクト
Optimal plans in Constrained Partially Observable Markov Decision Processes (CPOMDPs) maximize reward objectives while satisfying hard cost constraints, generalizing safe planning under state and transition uncertainty. Unfortunately, online CPOMDP planning is extremely difficult in large or continuous problem domains. In many large robotic domains, hierarchical decomposition can simplify planning by using tools for low-level control given high-level action primitives (options). We introduce Constrained Options Belief Tree Search (COBeTS) to leverage this hierarchy and scale online search-based CPOMDP planning to large robotic problems. We show that if primitive option controllers are defined to satisfy assigned constraint budgets, then COBeTS will satisfy constraints anytime. Otherwise, COBeTS will guide the search towards a safe sequence of option primitives, and hierarchical monitoring can be used to achieve runtime safety. We demonstrate COBeTS in several safety-critical, constrained partially observable robotic domains, showing that it can plan successfully in continuous CPOMDPs while non-hierarchical baselines cannot.
