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Robotics Institute
Seminar, February 2, 2007
Time and Place | Seminar Abstract | Speaker Biography | Speaker Appointments
Socially
Guided Machine Learning
Andrea Thomaz
Post-Doctoral Associate
MIT
Mauldin Auditorium (NSH
1305)
Refreshments 3:15 pm
Talk 3:30 pm
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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.
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Speaker Biography |
Andrea Thomaz is a
Post-Doctoral Associate in the Media Laboratory at the Massachusetts Institute
of Technology, where she also received her Ph.D. in 2006. Previously, Andrea
obtained a B.S. in Electrical and Computer Engineering from the
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Speaker Appointments |
For
appointments, please contact Janice Brochetti (janiceb@cs.cmu.edu)
The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.