Planning for Tabletop Object Rearrangement

TL;DR

This paper introduces an improved A*-based algorithm for tabletop object rearrangement planning that enhances scalability and solution quality, outperforming previous methods like orla* in efficiency and effectiveness.

Contribution

The paper presents an enhanced A*-based algorithm with better state representation and incremental goal attempts, addressing scalability issues in tabletop object rearrangement planning.

Findings

Superior solutions achieved faster than orla*

Effective for both stationary and mobile robots

Maintains high solution quality with increased scalability

Abstract

Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement, rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although orla* has proposed an A* based searching strategy with lazy evaluation for the high-quality solution, it is not scalable, with the success rate decreasing as the number of objects increases. To overcome this limitation, we propose an enhanced A*-based algorithm that improves state representation and employs incremental goal attempts with lazy evaluation at each iteration. This approach aims to enhance scalability while maintaining solution quality. Our evaluation demonstrates that our algorithm can provide superior solutions compared to orla*, in a shorter time, for both stationary and mobile robots.