MANER: Multi-Agent Neural Rearrangement Planning of Objects in Cluttered Environments
This addresses the need for efficient multi-robot collaboration in practical applications like warehouse management and home organization, representing an incremental advance over single-agent solutions.
The paper tackles the problem of multi-agent object rearrangement in cluttered environments by proposing a learning-based framework that iteratively selects objects, determines relocation regions, and pairs them with robots, resulting in improved traversal time and success rate compared to baselines.
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.