Object rearrangement is a fundamental problem in robotics with various practical applications ranging from managing warehouses to cleaning and organizing home kitchens. While existing research has primarily focused on single-agent solutions, real-world scenarios often require multiple robots to work together on rearrangement tasks. This paper proposes a comprehensive learning-based framework for multi-agent object rearrangement planning, addressing the challenges of task sequencing and path planning in complex environments. The proposed method iteratively selects objects, determines their relocation regions, and pairs them with available robots under kinematic feasibility and task reachability for execution to achieve the target arrangement. Our experiments on a diverse range of simulated and real-world environments demonstrate the effectiveness and robustness of the proposed framework. Furthermore, results indicate improved performance in terms of traversal time and success rate compared to baseline approaches.

翻译：物体重排是机器人学中的基本问题，具有从仓库管理到家庭厨房清洁整理等多种实际应用。现有研究主要聚焦于单智能体解决方案，但现实场景往往需要多机器人协作完成重排任务。本文提出了一种基于学习的综合框架，用于多智能体物体重排规划，解决了复杂环境中任务排序与路径规划的挑战。该方法通过迭代选择物体、确定其迁移区域，在运动学可行性与任务可达性约束下将物体与可用机器人配对以执行目标排列。我们在多种仿真和真实环境中的实验验证了所提框架的有效性与鲁棒性。此外，结果表明，与基线方法相比，该方法在遍历时间和成功率方面均有所提升。