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.

翻译：在约束部分可观测马尔可夫决策过程（CPOMDP）中，最优规划需在满足硬性成本约束的同时最大化奖励目标，这推广了状态与转移不确定性下的安全规划。然而，在线CPOMDP规划在大规模或连续问题领域中极其困难。在许多大型机器人领域，分层分解可通过利用低层控制工具（基于高层动作原语）来简化规划。我们提出了约束选项信念树搜索（COBeTS），以利用这种层级结构将基于搜索的在线CPOMDP规划扩展到大规模机器人问题。我们证明：若原始选项控制器被定义为满足分配约束预算，则COBeTS可在任意时刻满足约束；否则，COBeTS将引导搜索朝向安全的选项原语序列，并可通过分层监控实现运行时安全性。在多个安全关键的约束部分可观测机器人领域中的实验表明，COBeTS能在连续CPOMDP中成功规划，而无法实现分层规划的基线方法则无法做到。