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arXiv · 2410.23916

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

Davide Celestini·Daniele Gammelli·Tommaso Guffanti·Simone D'Amico·Elisa Capello·Marco Pavone
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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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Davide Celestini, Daniele Gammelli, Tommaso Guffanti, Simone D'Amico, Elisa Capello, Marco Pavone. 2024-10-31. Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling. https://doi.org/10.1109/lra.2024.3466069

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