Efficient Deep Learning of Robust Policies from MPC using Imitation and Tube-Guided Data Augmentation

Imitation learning (IL) can generate computationally efficient policies from demonstrations provided by model predictive control (MPC). However, IL methods often require extensive data-collection and training-efforts, limiting changes to the policy if the task changes, and they produce policies with limited robustness to new disturbances. In this work, we propose an IL strategy to efficiently compress a computationally expensive MPC into a deep neural network policy that is robust to previously unseen disturbances. By using a robust variant of the MPC, called robust tube MPC, and leveraging properties from the controller, we introduce computationally efficient data augmentation methods that enable a significant reduction of the number of MPC demonstrations and training efforts required to generate a robust policy. Our approach opens the possibility of zero-shot transfer of a policy trained from a single MPC demonstration collected in a nominal domain, such as a simulation or a robot in a lab/controlled environment, to a new domain with previously unseen bounded model errors/perturbations. Numerical evaluations performed using linear and nonlinear MPC for agile flight on a multirotor show that our method outperforms strategies commonly employed in IL (such as dataset-aggregation and domain randomization) in terms of demonstration-efficiency, training time, and robustness to perturbations unseen during training. Experimental evaluations validate the efficiency and real-world robustness.