Effectively Rearranging Heterogeneous Objects on Cluttered Tabletops

TL;DR

This paper presents advanced algorithms for the long-horizon sequential rearrangement of heterogeneous objects on cluttered tabletops, achieving near-optimal plans that consider object properties, with proven effectiveness in simulation and real robot experiments.

Contribution

It introduces state-of-the-art solvers for optimizing rearrangement plans considering object heterogeneity and clutter, advancing beyond previous grasp synthesis work.

Findings

Significant improvements in handling complex rearrangement tasks.

Effective planning strategies demonstrated in simulation and real robots.

Algorithms outperform existing methods in cluttered environments.

Abstract

Effectively rearranging heterogeneous objects constitutes a high-utility skill that an intelligent robot should master. Whereas significant work has been devoted to the grasp synthesis of heterogeneous objects, little attention has been given to the planning for sequentially manipulating such objects. In this work, we examine the long-horizon sequential rearrangement of heterogeneous objects in a tabletop setting, addressing not just generating feasible plans but near-optimal ones. Toward that end, and building on previous methods, including combinatorial algorithms and Monte Carlo tree search-based solutions, we develop state-of-the-art solvers for optimizing two practical objective functions considering key object properties such as size and weight. Thorough simulation studies show that our methods provide significant advantages in handling challenging heterogeneous object rearrangement problems, especially in cluttered settings. Real robot experiments further demonstrate and confirm these advantages. Source code and evaluation data associated with this research will be available at https://github.com/arc-l/TRLB upon the publication of this manuscript.