ロバストチューブMPCの模倣による実演効率の高い誘導方策探索
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
ロバストチューブMPCを模倣学習で深層ネットワークに圧縮し、チューブ特性を活かしたデータ拡張で実演効率と外乱ロバスト性を高めた。
著者: Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How
分類: cs.RO, cs.LG
原文アブストラクト
We propose a demonstration-efficient strategy to compress a computationally expensive Model Predictive Controller (MPC) into a more computationally efficient representation based on a deep neural network and Imitation Learning (IL). By generating a Robust Tube variant (RTMPC) of the MPC and leveraging properties from the tube, we introduce a data augmentation method that enables high demonstration-efficiency, being capable to compensate the distribution shifts typically encountered in IL. Our approach opens the possibility of zero-shot transfer from a single demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a domain with bounded model errors/perturbations. Numerical and experimental evaluations performed on a trajectory tracking MPC for a quadrotor show that our method outperforms strategies commonly employed in IL, such as DAgger and Domain Randomization, in terms of demonstration-efficiency and robustness to perturbations unseen during training.
