Latent Linear Quadratic Regulator for Robotic Control Tasks
<|reference_start|>Latent Linear Quadratic Regulator for Robotic Control Tasks: Model predictive control (MPC) has played a more crucial role in various robotic control tasks, but its high computational requirements are concerning, especially for nonlinear dynamical models. This paper presents a $\textbf{la}$tent $\textbf{l}$inear $\textbf{q}$uadratic $\textbf{r}$egulator (LaLQR) that maps the state space into a latent space, on which the dynamical model is linear and the cost function is quadratic, allowing the efficient application of LQR. We jointly learn this alternative system by imitating the original MPC. Experiments show LaLQR's superior efficiency and generalization compared to other baselines.<|reference_end|>
@article{zhang2024latent,
    title={Latent Linear Quadratic Regulator for Robotic Control Tasks},
    author={Yuan Zhang, Shaohui Yang, Toshiyuki Ohtsuka, Colin Jones, Joschka
  Boedecker},
    journal={arXiv preprint arXiv:2407.11107},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11107},
    primaryClass={cs.RO cs.LG}
}