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）在各种机器人控制任务中发挥着日益关键的作用，但其较高的计算需求令人担忧，尤其对于非线性动力学模型。本文提出了一种潜在线性二次调节器（LaLQR），它将状态空间映射到一个潜在空间，在该空间中动力学模型是线性的且代价函数是二次的，从而能够高效应用线性二次调节器（LQR）。我们通过模仿原始MPC来联合学习这一替代系统。实验表明，与其他基线方法相比，LaLQR具有更优的效率和泛化能力。