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：移动机器人任务规划通常假设环境知识完备，因此主流方法（如基于PDDL的规划）在任务关键物体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具实效，但作为独立工具运行，难以直接整合至完整任务规划器中——后者还需额外确定所需物体及其在规划中的搜索时机。为克服此局限，我们提出一种以新型基于模型的LIOS动作为核心的规划框架：每个动作均为旨在定位并获取单一物体的策略。高层规划将LIOS动作视为确定性操作，并依据基于模型的各动作期望成本计算，生成交替执行搜索与任务执行的规划方案，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。本工作能在保持与现有全知识求解器兼容性的同时，有效处理不确定性。在模拟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。