Multi-Robot Motion Planning with Diffusion Models

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:this https URL.

@article{shaoul2025_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 }
}