Multi-Robot Motion Planning with Diffusion Models
<|reference_start|>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 in our supplementary material, and our code at: https://github.com/yoraish/mmd.<|reference_end|>
@article{shaoul2024multi-robot,
    title={Multi-Robot Motion Planning with Diffusion Models},
    author={Yorai Shaoul, Itamar Mishani, Shivam Vats, Jiaoyang Li and Maxim
  Likhachev},
    journal={arXiv preprint arXiv:2410.03072},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.03072},
    primaryClass={cs.RO cs.AI cs.MA}
}