Hierarchical Object-Oriented POMDP Planning for Object Rearrangement

Rajesh Devaraddi Mangannavar, Alan Fern, Prasad Tadepalli

Keywords: rearrangement, POMDP, planning, reinforcement learning, object search

TL;DR: We propose a Hierarchical Object-Oriented POMDP planning framework for multi-object rearrangement in partially observable environments. We show it's effectiveness in the AI2Thor simulator experiments

Abstract: We present an online planning framework 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. We evaluate our system on varying numbers of objects, rooms, and problem types in AI2-THOR simulated environments with promising results.

Primary Area: applications to robotics, autonomy, planning

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Submission Number: 13503