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.

翻译：模型预测控制（MPC）在各类机器人控制任务中发挥着日益关键的作用，但其高计算需求令人担忧，尤其对于非线性动力学模型而言。本文提出了一种**潜在线性二次型调节器**（LaLQR），该方法将状态空间映射至一个潜在空间，在该空间中动力学模型呈线性且代价函数为二次型，从而使得线性二次型调节器（LQR）得以高效应用。我们通过模仿原始MPC的方式联合学习这一替代系统。实验表明，LaLQR相比其他基线方法具有更优越的效率和泛化能力。