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 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：物体重排是机器人领域的核心问题，广泛应用于仓库管理、厨房清洁整理等实际场景。现有研究主要关注单智能体解决方案，而现实任务往往需要多台机器人协同完成重排工作。本文提出一种基于学习的全面框架，用于解决复杂环境下的多智能体物体重排规划问题，涵盖任务排序与路径规划两大挑战。该方法通过迭代选择目标物体、确定迁移区域，并在运动学可行性及任务可达性约束下为可用机器人分配执行任务，最终实现目标布局。在多样化环境中的实验表明，该框架具有显著的有效性与鲁棒性。此外，相较于基线方法，本方法在路径耗时与任务成功率方面均表现出更优性能。