Problem
Research questions and friction points this paper is trying to address.
multi-robot path planning
scalability
generalization
dynamic environments
agent number variation
Train-Small Deploy-Large: Leveraging Diffusion-Based Multi-Robot Planning
🤖 AI Summary
Existing multi-robot path planning approaches exhibit limited generalization when scaled to larger systems and incur prohibitive training costs. This work introduces diffusion models to multi-robot path planning for the first time, proposing a novel “train small, deploy large” paradigm that supports dynamic robot counts. By training a shared diffusion model on a small number of robots and incorporating a dedicated inter-agent attention mechanism with temporal convolutions, the method enables efficient deployment to significantly larger-scale systems. Experimental results demonstrate that the proposed approach substantially outperforms current reinforcement learning and heuristic baselines across diverse scenarios, achieving notable advances in both scalability and planning accuracy.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
diffusion model
multi-robot path planning
train-small deploy-large
inter-agent attention
temporal convolution
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