Task planning for mobile robots often assumes full environment knowledge and so popular approaches, like planning via the PDDL, cannot plan when the locations of task-critical objects are unknown. Recent learning-driven object search approaches are effective, but operate as standalone tools and so are not straightforwardly incorporated into full task planners, which must additionally determine both what objects are necessary and when in the plan they should be sought out. To address this limitation, we develop a planning framework centered around novel model-based LIOS actions: each a policy that aims to find and retrieve a single object. High-level planning treats LIOS actions as deterministic and so -- informed by model-based calculations of the expected cost of each -- generates plans that interleave search and execution for effective, sound, and complete learning-informed task planning despite uncertainty. Our work effectively reasons about uncertainty while maintaining compatibility with existing full-knowledge solvers. In simulated ProcTHOR homes and in the real world, our approach outperforms non-learned and learned baselines on tasks including retrieval and meal prep.

翻译：移动机器人的任务规划通常假设具备完整的环境知识，因此诸如通过PDDL进行规划等主流方法在任务关键物体的位置未知时无法进行规划。近期基于学习的物体搜索方法虽然有效，但作为独立工具运行，难以直接整合到完整的任务规划器中——后者还需确定哪些物体是必需的以及应在计划何时进行搜寻。为突破这一局限，我们开发了一个以新型基于模型的LIOS动作为核心的规划框架：每个动作均为旨在寻找并获取单个物体的策略。高层规划将LIOS动作视为确定性操作，并基于对各动作预期成本的模型化计算，生成交替进行搜索与执行的规划方案，从而在不确定性条件下实现高效、可靠且完备的学习感知任务规划。本工作在对不确定性进行有效推理的同时，保持了与现有全知识求解器的兼容性。在模拟的ProcTHOR家居环境及现实场景中，我们的方法在物品检索与餐食制备等任务上均优于非学习型及学习型基线方法。