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 has played a more crucial role in various robotic control tasks%2C but its high computational requirements are concerning%2C especially for nonlinear dynamical models. This paper presents a %24textbf{la}%24tent %24textbf{l}%24inear %24textbf{q}%24uadratic %24textbf{r}%24egulator (LaLQR) that maps the state space into a latent space%2C on which the dynamical model is linear and the cost function is quadratic%2C 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.?quality=80&w=800)
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