Computational Benefits of Social Learning Mechanisms: Stimulus Enhancement and Emulation
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Abstract
Social learning in robotics has largely focused on
imitation learning. In this work, we take a broader view of
social learning and are interested in the multifaceted ways that a
social partner can influence the learning process. We implement
stimulus enhancement and emulation on a robot, and illustrate the
computational benefits of social learning over individual learning.
Additionally we characterize the differences between these two
social learning strategies, showing that the preferred strategy
is dependent on the current behavior of the social partner. We demonstrate these learning results both in simulation and with
physical robot ‘playmates’.
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2009
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