Learning from human teachers with Socially Guided Exploration

We present a learning mechanism, Socially Guided Exploration, in which a robot learns new tasks through a combination of self-exploration and social interaction. The system’s motivational drives (novelty, mastery), along with social scaffolding from a human partner, bias behavior to create learning opportunities for a Reinforcement Learning mechanism. The system is able to learn on its own, but can flexibly use the guidance of a human partner to improve performance. An experiment with non-expert human subjects shows a human is able to shape the learning process through suggesting actions and drawing attention to goal states. Human guidance results in a task set that is significantly more focused and efficient, while self exploration results in a broader set.

I. Introduction

Enabling a human to efficiently transfer knowledge and skills to a machine has inspired decades of research. When this work is viewed along a guidance-exploration spectrum, an interesting dichotomy appears. Prior works that incorporate human input into a Machine Learning process tend to maintain a constant level of human involvement.