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This document presents a novel adaptive model predictive control (MPC) method that utilizes Bayesian optimization and classifier learning to automatically estimate control parameters under model uncertainty and heteroscedastic noise. The proposed approach integrates a classifier into the model predictive path integral control framework, enhancing the robustness of controllers for various robotics tasks. The authors demonstrate the effectiveness of their method through experiments on classical control proble

Author

Trân Trân

Language

EN