(Georgia Institute of Technology, 2009)
Thomaz, Andrea L.; Cakmak, Maya
A general learning task for a robot in a new environment is
to learn about objects and what actions/effects they afford.
To approach this, we look at ways that a human partner
can intuitively help the robot learn, Socially Guided Machine
Learning. We present experiments conducted with
our robot, Junior, and make six observations characterizing
how people approached teaching about objects. We show
that Junior successfully used transparency to mitigate errors.
Finally, we present the impact of “social” versus “nonsocial”
data sets when training SVM classifiers.