Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC

• URL: http://arxiv.org/abs/2109.09910v1
• Date: Tue, 21 Sep 2021 01:50:19 GMT
• Title: Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
• Authors: Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How
• Abstract summary: We propose a 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. 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.
• Score: 36.3065978427856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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