تقرير
Latent Linear Quadratic Regulator for Robotic Control Tasks
| العنوان: | Latent Linear Quadratic Regulator for Robotic Control Tasks |
|---|---|
| المؤلفون: | Zhang, Yuan, Yang, Shaohui, Ohtsuka, Toshiyuki, Jones, Colin, Boedecker, Joschka |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Accepted at RSS 2024 workshop on Koopman Operators in Robotics |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2407.11107 |
| رقم الأكسشن: | edsarx.2407.11107 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |