Multi-Robot Motion Planning with Diffusion Models
Yorai Shaoul Itamar Mishani Shivam Vats Jiaoyang Li Maxim Likhachev
Diffusion models have recently been successfully applied to a wide range of robotics applications for learning complex multi-modal behaviors from data. However, prior works have mostly been confined to single-robot and small-scale environments due to the high sample complexity of learning multi-robot diffusion models. In this paper, we propose a method for generating collision-free multi-robot trajectories that conform to underlying data distributions while using only single-robot data. Our algorithm, Multi-robot Multi-model planning Diffusion (MMD), does so by combining learned diffusion models with classical search-based techniques---generating data-driven motions under collision constraints. Scaling further, we show how to compose multiple diffusion models to plan in large environments where a single diffusion model fails to generalize well. We demonstrate the effectiveness of our approach in planning for dozens of robots in a variety of simulated scenarios motivated by logistics environments.
Multi-Agent Planning, Robotics, Generative Models
This paper presents a method combining diffusion models with search-based techniques to generate scalable multi-robot trajectories using only single-robot data.