让扩散模型生成符合物理规律的控制轨迹，无需事先知道系统动力学。
Dynamics-aware Diffusion Models for Planning and Control
- 将系统动力学嵌入去噪过程，通过序列预测与投影确保轨迹合理。
- 在无动力学先验情况下仍能恢复线性反馈控制器轨迹，且达到最大似然。
- 适用于复杂场景下的路径规划与避障，适合机器人控制任务开发者。
本文针对复杂环境下控制任务中生成满足动态约束的轨迹问题，提出一种新型框架，通过序列预测与投影机制将系统动力学直接融入扩散模型的去噪过程。该机制与扩散模型的加噪调度对齐，确保生成轨迹既与专家示范一致，又满足底层物理约束。值得注意的是，本方法可在未显式提供动力学信息的情况下，生成最大似然轨迹，并准确恢复由线性反馈控制器生成的轨迹。我们在标准控制任务及包含航点追踪与避障的非凸最优控制问题上进行了验证，展示了其在实际应用中高效生成轨迹的潜力。代码已开源：www.github.com/darshangm/dynamics-aware-diffusion。
原文摘要 · Abstract (English)
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. We validate the effectiveness of our method through experiments on standard control tasks and a complex non-convex optimal control problem involving waypoint tracking and collision avoidance, demonstrating its potential for efficient trajectory generation in practical applications. Our code repository is available at www.github.com/darshangm/dynamics-aware-diffusion.
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