Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling

• URL: http://arxiv.org/abs/2410.23916v1
• Date: Thu, 31 Oct 2024 13:23:10 GMT
• Title: Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling
• Authors: Davide Celestini, Daniele Gammelli, Tommaso Guffanti, Simone D'Amico, Elisa Capello, Marco Pavone,
• Abstract summary: We present a unified framework combine the main strengths of optimization-based methods for learning. Our approach entails embedding high-capacity, transformer-based neural network models within optimization process. Compared to purely optimization-based approaches, results show that our approach can improve performance by up to 75%.
• Score: 16.112708478263745
• License:
• Abstract: Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization problems, leading to high computational complexity and strong dependency on initialization. In this work, we present a unified framework to combine the main strengths of optimization-based and learning-based methods for MPC. Our approach entails embedding high-capacity, transformer-based neural network models within the optimization process for trajectory generation, whereby the transformer provides a near-optimal initial guess, or target plan, to a non-convex optimization problem. Our experiments, performed in simulation and the real world onboard a free flyer platform, demonstrate the capabilities of our framework to improve MPC convergence and runtime. Compared to purely optimization-based approaches, results show that our approach can improve trajectory generation performance by up to 75%, reduce the number of solver iterations by up to 45%, and improve overall MPC runtime by 7x without loss in performance.

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