Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement

TL;DR

This paper presents task planning algorithms for dual-arm robot systems to efficiently solve complex, non-monotone tabletop object rearrangement tasks involving object dependencies and handoffs, achieving significant time savings.

Contribution

The paper introduces novel task planning algorithms specifically designed for dual-arm rearrangement, handling object dependencies and handoffs without relying on advanced motion planning.

Findings

Significant time savings over greedy and naive methods

Effective scheduling of pick-and-place sequences for dual-arm coordination

Demonstrated efficiency without sophisticated motion planners

Abstract

We investigate the problem of coordinating two robot arms to solve non-monotone tabletop multi-object rearrangement tasks. In a non-monotone rearrangement task, complex object-object dependencies exist that require moving some objects multiple times to solve an instance. In working with two arms in a large workspace, some objects must be handed off between the robots, which further complicates the planning process. For the challenging dual-arm tabletop rearrangement problem, we develop effective task planning algorithms for scheduling the pick-n-place sequence that can be properly distributed between the two arms. We show that, even without using a sophisticated motion planner, our method achieves significant time savings in comparison to greedy approaches and naive parallelization of single-robot plans.