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.

本研究解决了多机器人运动规划中的高样本复杂性和碰撞约束问题。提出了一种新的算法，名为多机器人多模型规划扩散（MMD），该算法结合了学习的扩散模型和经典的搜索技术，仅使用单机器人数据生成符合数据分布的无碰撞轨迹。实验表明，该方法能有效规划多个机器人在复杂环境中的运动，具有重要的应用潜力。

基于扩散模型的多机器人运动规划