Multi-Agent Monte Carlo Tree Search for Makespan-Efficient Object Rearrangement in Cluttered Spaces
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914301732716544 |
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| author | Ren, Hanwen Kim, Junyong Tharmasanthiran, Aathman Qureshi, Ahmed H. |
| author_facet | Ren, Hanwen Kim, Junyong Tharmasanthiran, Aathman Qureshi, Ahmed H. |
| contents | Object rearrangement planning in complex, cluttered environments is a common challenge in warehouses, households, and rescue sites. Prior studies largely address monotone instances, whereas real-world tasks are often non-monotone-objects block one another and must be temporarily relocated to intermediate positions before reaching their final goals. In such settings, effective multi-agent collaboration can substantially reduce the time required to complete tasks. This paper introduces Centralized, Asynchronous, Multi-agent Monte Carlo Tree Search (CAM-MCTS), a novel framework for general-purpose makespan-efficient object rearrangement planning in challenging environments. CAM-MCTS combines centralized task assignment-where agents remain aware of each other's intended actions to facilitate globally optimized planning-with an asynchronous task execution strategy that enables agents to take on new tasks at appropriate time steps, rather than waiting for others, guided by a one-step look-ahead cost estimate. This design minimizes idle time, prevents unnecessary synchronization delays, and enhances overall system efficiency. We evaluate CAM-MCTS across a diverse set of monotone and non-monotone tasks in cluttered environments, demonstrating consistent reductions in makespan compared to strong baselines. Finally, we validate our approach on a real-world multi-agent system under different configurations, further confirming its effectiveness and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02411 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Multi-Agent Monte Carlo Tree Search for Makespan-Efficient Object Rearrangement in Cluttered Spaces Ren, Hanwen Kim, Junyong Tharmasanthiran, Aathman Qureshi, Ahmed H. Robotics Object rearrangement planning in complex, cluttered environments is a common challenge in warehouses, households, and rescue sites. Prior studies largely address monotone instances, whereas real-world tasks are often non-monotone-objects block one another and must be temporarily relocated to intermediate positions before reaching their final goals. In such settings, effective multi-agent collaboration can substantially reduce the time required to complete tasks. This paper introduces Centralized, Asynchronous, Multi-agent Monte Carlo Tree Search (CAM-MCTS), a novel framework for general-purpose makespan-efficient object rearrangement planning in challenging environments. CAM-MCTS combines centralized task assignment-where agents remain aware of each other's intended actions to facilitate globally optimized planning-with an asynchronous task execution strategy that enables agents to take on new tasks at appropriate time steps, rather than waiting for others, guided by a one-step look-ahead cost estimate. This design minimizes idle time, prevents unnecessary synchronization delays, and enhances overall system efficiency. We evaluate CAM-MCTS across a diverse set of monotone and non-monotone tasks in cluttered environments, demonstrating consistent reductions in makespan compared to strong baselines. Finally, we validate our approach on a real-world multi-agent system under different configurations, further confirming its effectiveness and robustness. |
| title | Multi-Agent Monte Carlo Tree Search for Makespan-Efficient Object Rearrangement in Cluttered Spaces |
| topic | Robotics |
| url | https://arxiv.org/abs/2602.02411 |