Multi-Robot Motion Planning with Diffusion Models
- URL: http://arxiv.org/abs/2410.03072v1
- Date: Fri, 4 Oct 2024 01:31:13 GMT
- Title: Multi-Robot Motion Planning with Diffusion Models
- Authors: Yorai Shaoul, Itamar Mishani, Shivam Vats, Jiaoyang Li, Maxim Likhachev,
- Abstract summary: We propose a method for generating collision-free multi-robot trajectories.
Our algorithm combines learned diffusion models with classical search-based techniques.
We show how to compose multiple diffusion models to plan in large environments.
- Score: 22.08293753545732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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