  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.
