A Neural Network Architecture for Learning a Feedback Controller from Demonstration

Learning from demonstration (LfD) is an effective way of generating robot behaviors by transferring human demonstrated movements to robots. One common method to accomplish LfD is ‘Behavior Cloning’ (BC) with human-in-the-loop control, where the data obtained by human teleoperation of the robot is used to construct a non-linear controller by learning the state-to-action mapping. In this study, we propose a novel BC system where the learning architecture parallels a feedback controller that is tasked with producing the demonstrated motor output data. The motivation behind this design is that encoding such structure in the learning mechanism is expected to endow our system with prior bias to outperform controller-agnostic BC systems, especially in resource scarce situations. The current report presents the developed novel BC model, and gives its implementation for a two degrees-of-freedom robotic system as a proof of concept. To evaluate the performance of the system, systematic experiments are run in comparison with a controller-agnostic BC system. The results show that the proposed model performs significantly better than the baseline, indicating that it is a strong candidate for LfD tasks when the demonstrated data can be assumed to be generated through a feedback controller.