Social Learning Mechanisms for Robots

There is currently a surge of interest in service robotics—a desire to have robots leave the labs and factory floors to help solve critical issues facing our society, ranging from eldercare to education. A critical issue is that we cannot preprogram these robots with every skill they will need to play a useful role in society—they will need the ability to interact with ordinary people and acquire new relevant skills after they are deployed. Using human input with Machine Learning systems is not a new goal, but we believe that the problem needs reframing before the field will succeed in building robots that learn from everyday people. Many related works focus on machine performance gains; asking, “What can I get the person do to help my robot learn better?” In an approach we call, Socially Guided Machine Learning (SG-ML), we formulate the problem as a human-machine interaction; asking, “How can I improve the dynamics of this tightly coupled teacher-learner system?” With the belief that machines meant to learn from people can better take advantage of the ways in which people naturally approach teaching, our research aims to understand and computationally model mechanisms of human social learning in order to build machines that are natural and intuitive to teach. In this paper we focus on a particular aspect of SG-ML. When building a robot learner that takes advantage of human input, one of the design questions is “What is the right level of human guidance?” One has to determine how much and what kind of interaction to require of the human. We first review prior work with respect to these questions, and then summarize three recent projects. In the first two projects we investigate self versus social learning and demonstrate ways in which the two are mutually beneficial. In the third Andrea L. Thomaz, Maya Cakmak project we investigate a variety of social learning strategies, implementing four biologically inspired ways to take advantage of the social environment. We see computational benefits with each strategy depending on the environment, demonstrating the usefulness of non-imitative social learning. Taken together these projects argue that robots need a variety of learning strategies working together, including self and several types of social mechanisms, in order to succeed in Socially Guided Machine Learning.

Date

2009

Resource Type

Text

Resource Subtype

Proceedings
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