Feedback-Driven Incremental Imitation Learning Using Sequential VAE

Abstract

Variational Autoencoders (VAEs) have attracted a lot of attention from the machine learning community in recent years. The usage of VAEs in learning by demonstration and robotics is still very restricted due to the need for effective learning from only a few examples and due to the difficult evaluation of the reconstruction quality. In this paper, we utilize the current models of conditional variational autoencoders for the purpose of teaching a robot simple actions from demonstration in an incremental fashion. We in detail evaluate various training approaches and define parameters that are important for enabling high-quality samples and reconstructions. The quality of the generated samples in different stages of learning is evaluated both quantitatively and qualitatively on the humanoid robot Pepper. We show that the robot can reach a reasonable quality of generated actions already after 20 observed samples.