Hierarchical Object-Oriented POMDP Planning for Object Rearrangement

• URL: http://arxiv.org/abs/2412.01348v3
• Date: Mon, 25 Aug 2025 20:24:17 GMT
• Title: Hierarchical Object-Oriented POMDP Planning for Object Rearrangement
• Authors: Rajesh Mangannavar, Alan Fern, Prasad Tadepalli,
• Abstract summary: Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges.<n>To address this limitation, we introduce a novel Hierarchical Object-Oriented Partially Observed Markov Decision Process (HOO-POMDP) planning approach.<n>We present an online planning framework and a new benchmark dataset for solving multi-object rearrangement problems in partially observable, multi-room environments.
• Score: 19.62753215239688
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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