Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning
This addresses scalability and generalization issues in multi-robot systems, offering a practical solution for dynamic deployments, though it appears incremental as it builds on existing diffusion model techniques.
The paper tackles the problem of multi-robot path planning scaling poorly with increased numbers of robots during deployment, proposing a diffusion model-based planner that trains on a limited number of agents and generalizes effectively to larger numbers, achieving good accuracy in validation across multiple scenarios.
Learning based multi-robot path planning methods struggle to scale or generalize to changes, particularly variations in the number of robots during deployment. Most existing methods are trained on a fixed number of robots and may tolerate a reduced number during testing, but typically fail when the number increases. Additionally, training such methods for a larger number of agents can be both time consuming and computationally expensive. However, analytical methods can struggle to scale computationally or handle dynamic changes in the environment. In this work, we propose to leverage a diffusion model based planner capable of handling dynamically varying number of agents. Our approach is trained on a limited number of agents and generalizes effectively to larger numbers of agents during deployment. Results show that integrating a single shared diffusion model based planner with dedicated inter-agent attention computation and temporal convolution enables a train small deploy-large paradigm with good accuracy. We validate our method across multiple scenarios and compare the performance with existing multi-agent reinforcement learning techniques and heuristic control based methods.