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Latent Linear Quadratic Regulator for Robotic Control Tasks

arXiv cs.RO / 4/22/2026

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Key Points

  • The paper introduces LaLQR, a latent linear quadratic regulator method that transforms a robot’s state space into a latent space where the dynamics become linear and the cost becomes quadratic.
  • By working in the latent space, LaLQR enables more efficient use of LQR techniques for robotic control problems that would otherwise be computationally heavy for nonlinear model predictive control (MPC).
  • The approach learns the latent dynamical model and cost structure jointly by imitating the behavior of an existing (original) MPC controller.
  • Experiments report that LaLQR achieves better computational efficiency and stronger generalization than multiple baseline methods.
  • Overall, the work aims to reduce MPC’s computational burden while retaining robust control performance for nonlinear robotic dynamics.

Abstract

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.

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