AI RESEARCH
Latent Linear Quadratic Regulator for Robotic Control Tasks
arXiv CS.LG
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ArXi:2407.11107v3 Announce Type: replace-cross Model predictive control (MPC) has played a 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.