CL-DiffPhyCon: Closed-loop Diffusion Control of Complex Physical Systems
Long Wei, Haodong Feng, Yuchen Yang, Ruiqi Feng, Peiyan Hu, Xiang, Zheng, Tao Zhang, Dixia Fan, Tailin Wu
TL;DR
This paper introduces CL-DiffPhyCon, a novel closed-loop diffusion control method for complex physical systems that improves control performance and sampling efficiency by using asynchronous denoising and fast sampling techniques.
Contribution
It presents an efficient closed-loop diffusion control framework with asynchronous denoising and accelerated sampling for physical systems, addressing performance and efficiency challenges.
Findings
Achieves superior control performance on Burgers' equation and fluid control tasks.
Significantly reduces sampling computational cost.
Demonstrates effectiveness of fast sampling techniques like DDIM.
Abstract
The control problems of complex physical systems have broad applications in science and engineering. Previous studies have shown that generative control methods based on diffusion models offer significant advantages for solving these problems. However, existing generative control approaches face challenges in both performance and efficiency when extended to the closed-loop setting, which is essential for effective control. In this paper, we propose an efficient Closed-Loop Diffusion method for Physical systems Control (CL-DiffPhyCon). By employing an asynchronous denoising framework for different physical time steps, CL-DiffPhyCon generates control signals conditioned on real-time feedback from the system with significantly reduced computational cost during sampling. Additionally, the control process could be further accelerated by incorporating fast sampling techniques, such as DDIM.…
Peer Reviews
Decision·ICLR 2025 Poster
One advantage is to improve the computation efficiency, which is achieved by an asynchronous denoising framework. This aspect is validated using numerical simulations. Overall, this manuscript is well-written and organized.
There are some technical limitations, including noise-free inputs, input constraints, and numerical validations by using full in-domain control and observation.
The main strength of the proposed method is the idea to save computation by not fully denoising the latter steps of the control sequence, with the understanding that in receding horizon control, the controls for those latter stages will likely not be used, as new state observations will appear in the meantime and the control sequence will be replanned. This idea addresses one of the main shortcomings of controllers based on diffusion policies - the very long process of denoising during sampling,
I believe the proposed method is excellent and clearly applicable to very difficult control problems involving PDEs. However, most control problems are not of this kind. Why not show the performance on a simpler control problem with a handful of variables, hopefully not requiring high-end GPUs? This could potentially increase the applicability and appeal of the method significantly. Some minor typos: P. 3, L. 112: "It" -> "They" P. 3, L. 140: "has" -> "have" P. 7, L. 376: "well well" -> "perfo
The overall objective of the paper is interesting and timely: to design closed-loop diffusion-based control algorithms. The issue of computational complexity is important, since diffusion-based methods may work well but require extensive computations, making them difficult to use in feedback settings.
The paper is poorly presented, to an extent that makes the contributions unclear and difficult to appreciate. This start with the definition of the problem (where what is known/unknown/available to the designer is unclear, where there is confusion between system and environment, where the stated problem is inherently open-loop and the feedback structure is not evident), and continues into the description of the proposed method (the synchronous and asynchronous parts are vague and difficult to
Code & Models
Videos
Taxonomy
TopicsStability and Control of Uncertain Systems · Advanced Control Systems Optimization
MethodsDiffusion · SPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings