提出分层对象导向规划框架，解决多房间物体重排的感知不全问题。
Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
- 分层设计：对象导向POMDP生成子目标，低层策略执行，抽象系统适配高层规划
- 在10-30%初始可见性下仍稳定完成10-20个物体在2-4个房间的重排任务
- 新基准MultiRoomR包含路径阻塞、目标遮挡等复杂场景，适合评估鲁棒性
我们提出一种在线规划框架和新基准数据集，用于解决部分可观测、多房间环境中的多物体重排问题。现有方法多基于强化学习或人工编码规划，难以应对多样化挑战。为此，我们引入新型分层对象导向部分可观测马尔可夫决策过程（HOO-POMDP）规划方法，包含三部分：(a) 对象导向的POMDP规划器生成子目标，(b) 一组低层策略实现子目标，(c) 抽象系统将连续低层世界转化为适合抽象规划的表示。为实现对重排挑战的严格评估，我们构建了MultiRoomR基准，涵盖不同复杂度的多房间环境，具有10%-30%初始可见性、路径阻塞、目标遮挡以及分布在2-4个房间中的10-20个物体。实验表明，该系统能有效应对复杂场景，即使在感知不全时也保持稳健性能，在现有基准和新数据集上均取得良好结果。
原文摘要 · Abstract (English)
We present an online planning framework and a new benchmark dataset for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limitation, we introduce a novel Hierarchical Object-Oriented Partially Observed Markov Decision Process (HOO-POMDP) planning approach. This approach comprises of (a) an object-oriented POMDP planner generating sub-goals, (b) a set of low-level policies for sub-goal achievement, and (c) an abstraction system converting the continuous low-level world into a representation suitable for abstract planning. To enable rigorous evaluation of rearrangement challenges, we introduce MultiRoomR, a comprehensive benchmark featuring diverse multi-room environments with varying degrees of partial observability (10-30\% initial visibility), blocked paths, obstructed goals, and multiple objects (10-20) distributed across 2-4 rooms. Experiments demonstrate that our system effectively handles these complex scenarios while maintaining robust performance even with imperfect perception, achieving promising results across both existing benchmarks and our new MultiRoomR dataset.
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