Title:
Designing Interactions for Robot Active Learners
Designing Interactions for Robot Active Learners
Author(s)
Cakmak, Maya
Chao, Crystal
Thomaz, Andrea L.
Chao, Crystal
Thomaz, Andrea L.
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Abstract
This paper addresses some of the problems that arise
when applying active learning to the context of human–robot interaction
(HRI). Active learning is an attractive strategy for robot
learners because it has the potential to improve the accuracy and
the speed of learning, but it can cause issues from an interaction
perspective. Here we present three interaction modes that enable a
robot to use active learning queries. The three modes differ in when
they make queries: the first makes a query every turn, the second
makes a query only under certain conditions, and the third makes
a query only when explicitly requested by the teacher.We conduct
an experiment in which 24 human subjects teach concepts to our
upper-torso humanoid robot, Simon, in each interaction mode, and
we compare these modes against a baseline mode using only passive
supervised learning.We report results from both a learning and an
interaction perspective. The data show that the three modes using
active learning are preferable to the mode using passive supervised
learning both in terms of performance and human subject preference,
but each mode has advantages and disadvantages. Based on
our results, we lay out several guidelines that can inform the design
of future robotic systems that use active learning in an HRI setting.
Sponsor
This work was supported by the National Science Foundation, under
Grant IIS-0812106.
Date Issued
2010-06
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Article