Abstract: In this talk I present recent work from the Socially Intelligent
Machines Lab at Georgia Tech. One of the focuses of our lab is on
Socially Guided Machine Learning, building robot systems that can learn
from everyday human teachers. We look at standard Machine Learning
interactions and redesign interfaces and algorithms to support the
collection of learning input from naive humans. This talk covers
results on building computational models of reciprocal social
interactions, high-level task goal learning, low-level skill learning,
and active learning interactions using several humanoid robot platforms.
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