让机器人在不知物体位置时，自动规划搜索与执行顺序。
Effective Task Planning with Missing Objects using Learning-Informed Object Search
- 引入基于模型的单对象搜索动作（LIOS），实现智能搜索决策。
- 在模拟和真实环境中，任务完成率优于传统方法与纯学习方法。
- 兼容现有规划器，适合需要处理未知环境的机器人任务。
移动机器人任务规划通常假设环境信息完整，因此基于PDDL的主流方法在关键物体位置未知时无法工作。近年基于学习的物体搜索方法虽有效，但作为独立工具难以融入完整任务规划流程，而后者还需决定所需物体及搜索时机。为此，我们提出以新型基于模型的LIOS动作为核心的规划框架：每个LIOS动作是一个旨在定位并取回单一物体的策略。高层规划将LIOS动作视为确定性操作，依据模型计算的预期成本进行调度，生成交织搜索与执行的计划，在不确定性下仍能实现高效、可靠且完整的学习驱动任务规划。该方法在模拟的ProcTHOR家居环境和真实世界中，对物品取回与餐食准备等任务均优于非学习与学习基线方法。
原文摘要 · Abstract (English)
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.
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