High-Performance Dual-Arm Task and Motion Planning for Tabletop Rearrangement

TL;DR

This paper introduces SDAR, a dual-arm task and motion planning framework that effectively coordinates two robotic arms for complex tabletop rearrangement tasks, achieving high success rates and superior solution quality.

Contribution

The paper presents a novel integrated task and motion planning framework for dual-arm robots, combining dependency-driven task decomposition with layered motion planning for improved efficiency and success.

Findings

SDAR achieves 100% success rate on complex tasks.

SDAR outperforms previous state-of-the-art in solution quality.

The framework successfully transfers from simulation to real robots.

Abstract

We propose Synchronous Dual-Arm Rearrangement Planner (SDAR), a task and motion planning (TAMP) framework for tabletop rearrangement, where two robot arms equipped with 2-finger grippers must work together in close proximity to rearrange objects whose start and goal configurations are strongly entangled. To tackle such challenges, SDAR tightly knit together its dependency-driven task planner (SDAR-T) and synchronous dual-arm motion planner (SDAR-M), to intelligently sift through a large number of possible task and motion plans. Specifically, SDAR-T applies a simple yet effective strategy to decompose the global object dependency graph induced by the rearrangement task, to produce more optimal dual-arm task plans than solutions derived from optimal task plans for a single arm. Leveraging state-of-the-art GPU SIMD-based motion planning tools, SDAR-M employs a layered motion planning strategy to sift through many task plans for the best synchronous dual-arm motion plan while ensuring high levels of success rate. Comprehensive evaluation demonstrates that SDAR delivers a 100% success rate in solving complex, non-monotone, long-horizon tabletop rearrangement tasks with solution quality far exceeding the previous state-of-the-art. Experiments on two UR-5e arms further confirm SDAR directly and reliably transfers to robot hardware. Source code and supplementary materials are available at https://github.com/arc-l/dual-arm.