# Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting

arXiv: https://arxiv.org/abs/1912.07024 · HTML: https://mlsanity.com/paper/1912.07024/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Dec 2019.

## Built on by (most cited first, 13 of 13)

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- [Visual Room Rearrangement](https://mlsanity.com/paper/2103.16544/) (2021)
- [Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement](https://mlsanity.com/paper/2111.07975/) (2021)
- [Transporters with Visual Foresight for Solving Unseen Rearrangement Tasks](https://mlsanity.com/paper/2202.10765/) (2022)
- [Structural Concept Learning via Graph Attention for Multi-Level Rearrangement Planning](https://mlsanity.com/paper/2309.02547/) (2023)
- [Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces](https://mlsanity.com/paper/2110.02814/) (2021)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A Star](https://mlsanity.com/paper/2309.13707/) (2023)
- [Hierarchical Policy for Non-prehensile Multi-object Rearrangement with Deep Reinforcement Learning and Monte Carlo Tree Search](https://mlsanity.com/paper/2109.08973/) (2021)
- [Learning Dual-arm Object Rearrangement for Cartesian Robots](https://mlsanity.com/paper/2402.13634/) (2024)
- [Robotic Test Tube Rearrangement Using Combined Reinforcement Learning and Motion Planning](https://mlsanity.com/paper/2401.09772/) (2024)
- [Dynamic Buffers: Cost-Efficient Planning for Tabletop Rearrangement with Stacking](https://mlsanity.com/paper/2509.22828/) (2025)
- [Optimal Knock-Pick Planning for Tightly Packed Tabletop Blocks With Parallel Grippers](https://mlsanity.com/paper/2605.17800/) (2026)

## Abstract (arXiv)

In this work, we address a planar non-prehensile sorting task. Here, a robot needs to push many densely packed objects belonging to different classes into a configuration where these classes are clearly separated from each other. To achieve this, we propose to employ Monte Carlo tree search equipped with a task-specific heuristic function. We evaluate the algorithm on various simulated and real-world sorting tasks. We observe that the algorithm is capable to reliably sort large numbers of convex and non-convex objects, as well as convex objects in the presence of immovable obstacles.