# Planning for Manipulation among Movable Objects: Deciding Which Objects Go Where, in What Order, and How

arXiv: https://arxiv.org/abs/2303.13385 · HTML: https://mlsanity.com/paper/2303.13385/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Estimating Continuous Distributions in Bayesian Classifiers](https://mlsanity.com/paper/1302.4964/) (2013)
- [Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement](https://mlsanity.com/paper/2105.02857/) (2021)
- [Manipulation Planning Among Movable Obstacles Using Physics-Based Adaptive Motion Primitives](https://mlsanity.com/paper/2102.04324/) (2021)
- [Persistent Homology for Effective Non-Prehensile Manipulation](https://mlsanity.com/paper/2202.02937/) (2022)
- [Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments](https://mlsanity.com/paper/1902.06907/) (2019)
- [Fast and resilient manipulation planning for target retrieval in clutter](https://mlsanity.com/paper/2003.11420/) (2020)
- [Synchronized Multi-Arm Rearrangement Guided by Mode Graphs with Capacity Constraints](https://mlsanity.com/paper/2005.09127/) (2020)
- [Planning with Selective Physics-based Simulation for Manipulation Among Movable Objects](https://mlsanity.com/paper/2003.06743/) (2020)
- [Semi-Autonomous Teleoperation via Learning Non-Prehensile Manipulation Skills](https://mlsanity.com/paper/2109.13081/) (2021)
- [Lazy Rearrangement Planning in Confined Spaces](https://mlsanity.com/paper/2203.10379/) (2022)

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

- [MOSAIC: Skill-Centric Manipulation Planning with Physics Simulation](https://mlsanity.com/paper/2504.16738/) (2025)
- [Map Space Belief Prediction for Manipulation-Enhanced Mapping](https://mlsanity.com/paper/2502.20606/) (2025)

## Abstract (arXiv)

We are interested in pick-and-place style robot manipulation tasks in cluttered and confined 3D workspaces among movable objects that may be rearranged by the robot and may slide, tilt, lean or topple. A recently proposed algorithm, M4M, determines which objects need to be moved and where by solving a Multi-Agent Pathfinding MAPF abstraction of this problem. It then utilises a nonprehensile push planner to compute actions for how the robot might realise these rearrangements and a rigid body physics simulator to check whether the actions satisfy physics constraints encoded in the problem. However, M4M greedily commits to valid pushes found during planning, and does not reason about orderings over pushes if multiple objects need to be rearranged. Furthermore, M4M does not reason about other possible MAPF solutions that lead to different rearrangements and pushes. In this paper, we extend M4M and present Enhanced-M4M (E-M4M) -- a systematic graph search-based solver that searches over orderings of pushes for movable objects that need to be rearranged and different possible rearrangements of the scene. We introduce several algorithmic optimisations to circumvent the increased computational complexity, discuss the space of problems solvable by E-M4M and show that experimentally, both on the real robot and in simulation, it significantly outperforms the original M4M algorithm, as well as other state-of-the-art alternatives when dealing with complex scenes.