Fast High-Quality Tabletop Rearrangement in Bounded Workspace

TL;DR

This paper presents a fast, robust planning method for rearranging multiple objects on a cluttered tabletop using overhand grasps, efficiently handling buffer placement and outperforming existing approaches in large-scale scenarios.

Contribution

The paper introduces a two-step baseline planner combined with a lazy tree search and novel preprocessing, significantly improving solution speed and robustness for complex tabletop rearrangement tasks.

Findings

The proposed method generates high-quality solutions quickly.

It outperforms state-of-the-art approaches in large-scale instances.

The approach is robust in cluttered environments.

Abstract

In this paper, we examine the problem of rearranging many objects on a tabletop in a cluttered setting using overhand grasps. Efficient solutions for the problem, which capture a common task that we solve on a daily basis, are essential in enabling truly intelligent robotic manipulation. In a given instance, objects may need to be placed at temporary positions ("buffers") to complete the rearrangement, but allocating these buffer locations can be highly challenging in a cluttered environment. To tackle the challenge, a two-step baseline planner is first developed, which generates a primitive plan based on inherent combinatorial constraints induced by start and goal poses of the objects and then selects buffer locations assisted by the primitive plan. We then employ the "lazy" planner in a tree search framework which is further sped up by adapting a novel preprocessing routine. Simulation experiments show our methods can quickly generate high-quality solutions and are more robust in solving large-scale instances than existing state-of-the-art approaches. source:github.com/arc-l/TRLB