Faster Model Predictive Control via Self-Supervised Initialization Learning
<|reference_start|>Faster Model Predictive Control via Self-Supervised Initialization Learning: Optimization for robot control tasks, spanning various methodologies, includes Model Predictive Control (MPC). However, the complexity of the system, such as non-convex and non-differentiable cost functions and prolonged planning horizons often drastically increases the computation time, limiting MPC's real-world applicability. Prior works in speeding up the optimization have limitations on solving convex problem and generalizing to hold out domains. To overcome this challenge, we develop a novel framework aiming at expediting optimization processes. In our framework, we combine offline self-supervised learning and online fine-tuning through reinforcement learning to improve the control performance and reduce optimization time. We demonstrate the effectiveness of our method on a novel, challenging Formula-1-track driving task, achieving 3.9\% higher performance in optimization time and 3.6\% higher performance in tracking accuracy on challenging holdout tracks.<|reference_end|>
@article{li2024faster,
    title={Faster Model Predictive Control via Self-Supervised Initialization
  Learning},
    author={Zhaoxin Li, Letian Chen, Rohan Paleja, Subramanya Nageshrao, Matthew
  Gombolay},
    journal={arXiv preprint arXiv:2408.03394},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03394},
    primaryClass={cs.RO}
}