分類器学習による適応型モデル予測制御
Adaptive Model Predictive Control by Learning Classifiers
ベイズ最適化を密度比推定として分類器学習で近似し、モデル予測パス積分制御に統合することで、モデル不確実性や異分散ノイズ下でも制御パラメータを自動調整する適応型MPCを提案した論文。
著者: Rel Guzman, Rafael Oliveira, Fabio Ramos
分類: cs.RO, cs.AI, cs.LG, cs.SY, eess.SY
原文アブストラクト
Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the presence of environment disturbances. Despite the successes, it is still unclear how to best adjust control parameters to the current task in the presence of model parameter uncertainty and heteroscedastic noise. In this paper, we propose an adaptive MPC variant that automatically estimates control and model parameters by leveraging ideas from Bayesian optimisation (BO) and the classical expected improvement acquisition function. We leverage recent results showing that BO can be reformulated via density ratio estimation, which can be efficiently approximated by simply learning a classifier. This is then integrated into a model predictive path integral control framework yielding robust controllers for a variety of challenging robotics tasks. We demonstrate the approach on classical control problems under model uncertainty and robotics manipulation tasks.
