# Task Planning for Object Rearrangement in Multi-room Environments

arXiv: https://arxiv.org/abs/2406.00451 · HTML: https://mlsanity.com/paper/2406.00451/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2024.

## Built on by (most cited first, 2 of 2)

- [Hierarchical Object-Oriented POMDP Planning for Object Rearrangement](https://mlsanity.com/paper/2412.01348/) (2024)
- [Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking](https://mlsanity.com/paper/2509.22828/) (2025)

## Abstract (arXiv)

Object rearrangement in a multi-room setup should produce a reasonable plan that reduces the agent's overall travel and the number of steps. Recent state-of-the-art methods fail to produce such plans because they rely on explicit exploration for discovering unseen objects due to partial observability and a heuristic planner to sequence the actions for rearrangement. This paper proposes a novel hierarchical task planner to efficiently plan a sequence of actions to discover unseen objects and rearrange misplaced objects within an untidy house to achieve a desired tidy state. The proposed method introduces several novel techniques, including (i) a method for discovering unseen objects using commonsense knowledge from large language models, (ii) a collision resolution and buffer prediction method based on Cross-Entropy Method to handle blocked goal and swap cases, (iii) a directed spatial graph-based state space for scalability, and (iv) deep reinforcement learning (RL) for producing an efficient planner. The planner interleaves the discovery of unseen objects and rearrangement to minimize the number of steps taken and overall traversal of the agent. The paper also presents new metrics and a benchmark dataset called MoPOR to evaluate the effectiveness of the rearrangement planning in a multi-room setting. The experimental results demonstrate that the proposed method effectively addresses the multi-room rearrangement problem.