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 work 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.
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