CMU RI Seminar: Socially Guided Machine Learning

Andrea Thomaz
Post-Doctoral Associate
MIT

Abstract
There is a surge of interest in having 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 will not be able to preprogram these robots with every skill they will need to play a useful role in society; robots will need the ability to interact and learn new things 'on the job' from everyday people. This talk introduces a paradigm, Socially Guided Machine Learning, that reframes the Machine Learning problem as a human-machine interaction, asking: How can systems be designed to take better advantage of learning from a human partner and the ways that everyday people approach the task of teaching?

In this talk I describe two novel social learning systems, on robotic and computer game platforms. Results from these systems show that designing agents to better fit human expectations of a social learning partner both improves the interaction for the human and significantly improves the way machines learn.

Sophie is a virtual robot that learns from human players in a video game via interactive Reinforcement Learning. A series of experiments with this platform uncovered and explored three principles of Social Machine Learning: guidance, transparency, and asymmetry. For example, everyday people were able to use an attention direction signal to significantly improve learning on many dimensions: a 50% decrease in actions needed to learn a task, and a 40% decrease in task failures during training.

On the Leonardo social robot, I describe my work enabling Leo to participate in social learning interactions with a human partner. Examples include learning new tasks in a tutelage paradigm, learning via guided exploration, and learning object appraisals through social referencing. An experiment with human subjects shows that Leo's social mechanisms significantly reduced teaching time by aiding in error detection and correction.