MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments

Object rearrangement is a fundamental prob- lem 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 mul- tiple 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 avail- able 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 perfor- mance in terms of traversal time and success rate compared to baseline approaches. The videos and supplementary mate- rial are available at https://sites.google.com/view/ maner-supplementary