This work focuses on the dual-arm object rearrangement problem abstracted
from a realistic industrial scenario of Cartesian robots. The goal of this
problem is to transfer all the objects from sources to targets with the minimum
total completion time. To achieve the goal, the core idea is to develop an
effective object-to-arm task assignment strategy for minimizing the cumulative
task execution time and maximizing the dual-arm cooperation efficiency. One of
the difficulties in the task assignment is the scalability problem. As the
number of objects increases, the computation time of traditional
offline-search-based methods grows strongly for computational complexity.
Encouraged by the adaptability of reinforcement learning (RL) in long-sequence
task decisions, we propose an online task assignment decision method based on
RL, and the computation time of our method only increases linearly with the
number of objects. Further, we design an attention-based network to model the
dependencies between the input states during the whole task execution process
to help find the most reasonable object-to-arm correspondence in each task
assignment round. In the experimental part, we adapt some search-based methods
to this specific setting and compare our method with them. Experimental result
shows that our approach achieves outperformance over search-based methods in
total execution time and computational efficiency, and also verifies the
generalization of our method to different numbers of objects. In addition, we
show the effectiveness of our method deployed on the real robot in the
supplementary video.

本研究针对工业机器人中的双臂物体重排问题，提出了一种基于强化学习和注意力网络的任务指派策略，实现了最小化任务执行时间和最大化双臂协作效率，实验结果表明该方法在总执行时间和计算效率方面优于传统方法，并且在不同物体数量下的推广性得到了验证。