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.

本文通过引入深度神经网络和模仿学习，提出了一种高效的方法来将计算成本昂贵的模型预测控制器(MPC)压缩成更高效的表示，首次提出了Robust Tube variant(RTMPC)和数据增强方法来弥补通常在模仿学习中遇到的分布偏移问题，并通过数值和实验评估表明，相对于常用的仿真方法，如DAgger和域拓扑，我们的方法在演示效率和对训练期间未见过的扰动的抗干扰性方面表现更好。

基于鲁棒管模型预测控制的演示高效引导策略搜索