# Efficient and High-quality Prehensile Rearrangement in Cluttered and Confined Spaces

arXiv: https://arxiv.org/abs/2110.02814 · HTML: https://mlsanity.com/paper/2110.02814/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 56 references that are in the map.
- First posted on arXiv 6 Oct 2021.

## Builds on (most cited first, 19 of 19)

- [Transporter Networks: Rearranging the Visual World for Robotic Manipulation](https://mlsanity.com/paper/2010.14406/) (2020)
- [PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning](https://mlsanity.com/paper/1802.08705/) (2018)
- [Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter](https://mlsanity.com/paper/1903.01588/) (2019)
- [Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands](https://mlsanity.com/paper/2003.03518/) (2020)
- [Monte-Carlo Tree Search for Efficient Visually Guided Rearrangement Planning](https://mlsanity.com/paper/1904.10348/) (2019)
- [Where to relocate?: Object rearrangement inside cluttered and confined environments for robotic manipulation](https://mlsanity.com/paper/2003.10863/) (2020)
- [Visual Foresight Trees for Object Retrieval from Clutter with Nonprehensile Rearrangement](https://mlsanity.com/paper/2105.02857/) (2021)
- [Multi-Object Rearrangement with Monte Carlo Tree Search:A Case Study on Planar Nonprehensile Sorting](https://mlsanity.com/paper/1912.07024/) (2019)
- [Occlusion-Aware Search for Object Retrieval in Clutter](https://mlsanity.com/paper/2011.03334/) (2020)
- [Complexity Results and Fast Methods for Optimal Tabletop Rearrangement with Overhand Grasps](https://mlsanity.com/paper/1711.07369/) (2017)
- [Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning](https://mlsanity.com/paper/1803.05752/) (2018)
- [Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search](https://mlsanity.com/paper/2101.12241/) (2021)
- [Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments](https://mlsanity.com/paper/1902.06907/) (2019)
- [DIPN: Deep Interaction Prediction Network with Application to Clutter Removal](https://mlsanity.com/paper/2011.04692/) (2020)
- [Toward Fast and Optimal Robotic Pick-and-Place on a Moving Conveyor](https://mlsanity.com/paper/1912.08009/) (2019)
- [On Minimizing the Number of Running Buffers for Tabletop Rearrangement](https://mlsanity.com/paper/2105.06357/) (2021)
- [Decision Making in Joint Push-Grasp Action Space for Large-Scale Object Sorting](https://mlsanity.com/paper/2010.10064/) (2020)
- [Space-Aware Reconfiguration](https://mlsanity.com/paper/2006.04402/) (2020)
- [Safe and Effective Picking Paths in Clutter given Discrete Distributions of Object Poses](https://mlsanity.com/paper/2008.04465/) (2020)

## Built on by (most cited first, 6 of 6)

- [Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement](https://mlsanity.com/paper/2207.08078/) (2022)
- [Grasp, See, and Place: Efficient Unknown Object Rearrangement with Policy Structure Prior](https://mlsanity.com/paper/2402.15402/) (2024)
- [Mechanical Search on Shelves using a Novel "Bluction" Tool](https://mlsanity.com/paper/2201.08968/) (2022)
- [Lazy Rearrangement Planning in Confined Spaces](https://mlsanity.com/paper/2203.10379/) (2022)
- [SPOTS: Stable Placement of Objects with Reasoning in Semi-Autonomous Teleoperation Systems](https://mlsanity.com/paper/2309.13937/) (2023)
- [Robotic Test Tube Rearrangement Using Combined Reinforcement Learning and Motion Planning](https://mlsanity.com/paper/2401.09772/) (2024)

## Abstract (arXiv)

Prehensile object rearrangement in cluttered and confined spaces has broad applications but is also challenging. For instance, rearranging products in a grocery shelf means that the robot cannot directly access all objects and has limited free space. This is harder than tabletop rearrangement where objects are easily accessible with top-down grasps, which simplifies robot-object interactions. This work focuses on problems where such interactions are critical for completing tasks. It proposes a new efficient and complete solver under general constraints for monotone instances, which can be solved by moving each object at most once. The monotone solver reasons about robot-object constraints and uses them to effectively prune the search space. The new monotone solver is integrated with a global planner to solve non-monotone instances with high-quality solutions fast. Furthermore, this work contributes an effective pre-processing tool to significantly speed up online motion planning queries for rearrangement in confined spaces. Experiments further demonstrate that the proposed monotone solver, equipped with the pre-processing tool, results in 57.3% faster computation and 3 times higher success rate than state-of-the-art methods. Similarly, the resulting global planner is computationally more efficient and has a higher success rate, while producing high-quality solutions for non-monotone instances (i.e., only 1.3 additional actions are needed on average). Videos of demonstrating solutions on a real robotic system and codes can be found at https://github.com/Rui1223/uniform_object_rearrangement.