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Multi-Robot Motion Planning with Diffusion Models
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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: https://multi-robot-diffusion.github.io/.
Forward citations
Cited by 7 Pith papers
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SwarmDiff: Swarm Robotic Trajectory Planning in Cluttered Environments via Diffusion Transformer
A hierarchical diffusion-transformer planner for robot swarms that generates GMM trajectories via cost-guided sampling and tracks them with distributed MPC.
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