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Diffusion Models for Fast and Accurate Approximate Model Predictive Control
Model predictive control (MPC) is a powerful control and planning framework for a large class of problems, yet its practical application remains limited by computational demands. While previous efforts have focused on approximating MPC with explicit representations for high-frequency real-time deployment, handling complex MPC formulations with multiple local optima or set-valued global optima remains an open challenge in practice. This thesis explores the use of diffusion models for approximate MPC, enabling their application in such scenarios with low computational time. We introduce a novel diffusion-based approximator capable of accurately modeling multi-modal out- put distributions, while achieving computation times under 2.5 ms, allowing users to efficiently sample multiple feasible and locally optimal solutions with no additional computational overhead. Our method is quantitatively compared with traditional least-squares regression models, demonstrating significant improvements. Experimental validation is performed on a 7-DOF KUKA LBR4+ robotic arm operating at 250 Hz, confirming the benefits of our approach and providing insights into high-frequency neural control. Additionally, we examine diffusion model sampling strategies, leveraging their unique properties to ensure feasible and smooth closed-loop operation. As part of this work, we release a general software framework for data collection using optimal control policies in the photo-realistic simulator Isaac Lab. The framework includes multi-processing tools for CPU-based controllers and supports training and evaluating neural controllers, including diffusion models such as DDPM and traditional least-squares regression.
@mastersthesis{Marquez-Julbe24-M-Diffusion, title = {Diffusion Models for Fast and Accurate Approximate Model Predictive Control}, abstract = {Model predictive control (MPC) is a powerful control and planning framework for a large class of problems, yet its practical application remains limited by computational demands. While previous efforts have focused on approximating MPC with explicit representations for high-frequency real-time deployment, handling complex MPC formulations with multiple local optima or set-valued global optima remains an open challenge in practice. This thesis explores the use of diffusion models for approximate MPC, enabling their application in such scenarios with low computational time. We introduce a novel diffusion-based approximator capable of accurately modeling multi-modal out- put distributions, while achieving computation times under 2.5 ms, allowing users to efficiently sample multiple feasible and locally optimal solutions with no additional computational overhead. Our method is quantitatively compared with traditional least-squares regression models, demonstrating significant improvements. Experimental validation is performed on a 7-DOF KUKA LBR4+ robotic arm operating at 250 Hz, confirming the benefits of our approach and providing insights into high-frequency neural control. Additionally, we examine diffusion model sampling strategies, leveraging their unique properties to ensure feasible and smooth closed-loop operation. As part of this work, we release a general software framework for data collection using optimal control policies in the photo-realistic simulator Isaac Lab. The framework includes multi-processing tools for CPU-based controllers and supports training and evaluating neural controllers, including diffusion models such as DDPM and traditional least-squares regression.}, degree_type = {Master}, school = {Eindhoven University of Technology}, address = {Eindhoven, the Netherlands}, month = dec, year = {2024}, note = {Master of Science in Systems and Control}, slug = {marquez-julbe24-m-diffusion}, author = {Marquez Julbe, Pau}, month_numeric = {12} }