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

TL;DR

This paper introduces an efficient solver and global planner for prehensile object rearrangement in cluttered, confined spaces, significantly improving speed and success rates over existing methods.

Contribution

It presents a novel monotone solver with a pre-processing tool for faster online planning and high-quality solutions in complex rearrangement tasks.

Findings

57.3% faster computation compared to state-of-the-art methods

3 times higher success rate in rearrangement tasks

On average, only 1.3 additional actions needed for non-monotone instances

Abstract

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.