Experiments in Socially Guided Machine Learning: Understanding Human Intent of Reward/Punishment
Proceedings of the ACM/IEEE international conference on Human-Robot Interaction
Abstract
In Socially Guided Machine Learning we explore the ways in which machine learning can more fully take advantage of natural human interaction. In this paper we are studying the role real-time human interaction plays in training assistive robots to perform new tasks. We describe an experimental platform, Sophie’s World, and present descriptive analysis of human teaching behavior found in a user study. We report three important observations of how people administer reward and punishment to teach a simulated robot a new task through Reinforcement Learning. People adjust their behavior as they develop a model of the learner, they use the reward channel for guidance as well as feedback, and they may also use it as a motivational channel.