Tutelage and socially guided robot learning

Abstract

We view the problem of machine learning as a collaboration between the human and the machine. Inspired by human-style tutelage, we situate the learning problem within a dialog in which social interaction structures the learning experience, providing instruction, directing attention, and controlling the complexity of the task. We present a learning mechanism, implemented on a humanoid robot, to demonstrate that a collaborative dialog framework allows a robot to efficiently learn a task from a human, generalize this ability to a new task configuration, and show commitment to the overall goal of the learned task. We also compare this approach to traditional machine learning approaches.