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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.11107 |
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| _version_ | 1866918457935659008 |
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| author | Zhang, Yuan Yang, Shaohui Ohtsuka, Toshiyuki Jones, Colin Boedecker, Joschka |
| author_facet | Zhang, Yuan Yang, Shaohui Ohtsuka, Toshiyuki Jones, Colin Boedecker, Joschka |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_11107 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Latent Linear Quadratic Regulator for Robotic Control Tasks Zhang, Yuan Yang, Shaohui Ohtsuka, Toshiyuki Jones, Colin Boedecker, Joschka Robotics Machine Learning 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. |
| title | Latent Linear Quadratic Regulator for Robotic Control Tasks |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2407.11107 |