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.

翻译：扩散模型近期已成功应用于广泛的机器人学领域，用于从数据中学习复杂的多模态行为。然而，由于学习多机器人扩散模型的高样本复杂性，先前的研究大多局限于单机器人及小规模环境。本文提出一种方法，仅利用单机器人数据即可生成符合底层数据分布且无碰撞的多机器人轨迹。我们的算法——多机器人多模型规划扩散（MMD）——通过将学习到的扩散模型与经典的基于搜索的技术相结合，在碰撞约束下生成数据驱动的运动。为进一步扩展规模，我们展示了如何组合多个扩散模型以在单个扩散模型泛化能力不足的大规模环境中进行规划。我们在受物流环境启发的多种仿真场景中，对数十个机器人进行规划，验证了所提方法的有效性。视频演示请参见补充材料，代码位于：https://github.com/yoraish/mmd。