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. View video demonstrations and code at: https://multi-robot-diffusion.github.io/.
@article{arxiv.2410.03072,
title = {Multi-Robot Motion Planning with Diffusion Models},
author = {Yorai Shaoul and Itamar Mishani and Shivam Vats and Jiaoyang Li and Maxim Likhachev},
journal= {arXiv preprint arXiv:2410.03072},
year = {2025}
}
Comments
The first three authors contributed equally to this work. Published at ICLR 2025