The field of robotics is witnessing significant advancements in multi-robot motion planning, with a focus on developing efficient and scalable methods for coordinating multiple robots in shared workspaces. Recent developments have centered around leveraging graph neural networks, reinforcement learning, and diffusion models to address the challenges of task allocation, scheduling, and motion planning in complex environments. These innovative approaches have shown promise in improving the speed and scalability of motion planning, enabling fault-tolerant planning and online perception-based re-planning. Notable papers in this area include:

• RoboBallet, which proposes a reinforcement learning framework for automated task and motion planning in obstacle-rich environments.
• PegasusFlow, which introduces a hierarchical rolling-denoising framework for parallel sampling of trajectory score gradients, bypassing the need for expert data.
• Joint Model-based Model-free Diffusion, which formulates module integration as a joint sampling problem to maximize compatibility between model-free diffusion planners and model-based optimization modules.