Dynamics-aware Diffusion Models for Planning and Control

Abstract-- This paper addresses the problem of generating dynamically admissible trajectories for control tasks using diffusion models, particularly in scenarios where the environment is complex and system dynamics are crucial for practical application. We propose a novel framework that integrates system dynamics directly into the diffusion model's denoising process through a sequential prediction and projection mechanism. This mechanism, aligned with the diffusion model's noising schedule, ensures generated trajectories are both consistent with expert demonstrations and adhere to underlying physical constraints. Notably, our approach can generate maximum likelihood trajectories and accurately recover trajectories generated by linear feedback controllers, even when explicit dynamics knowledge is unavailable. Our code repository is available at www.github.com/ Diffusion models have emerged as powerful tools for learning complex data distributions, demonstrating significant potential in control and robotics, particularly for high-dimensional trajectory generation [1]. Their ability to learn and replicate expert demonstrations makes them attractive for imitation learning and decision-making. However, a critical limitation arises from their inherent lack of explicit dynamics awareness.