Title:
Modeling Robot Differences by Leveraging a Physically Shared Context
Modeling Robot Differences by Leveraging a Physically Shared Context
Author(s)
Kira, Zsolt
Long, Kathryn
Long, Kathryn
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Abstract
Knowledge sharing, either implicit or
explicit, is crucial during development as
evidenced by many studies into the transfer of
knowledge by teachers via gaze following and
learning by imitation. In the future, the teacher
of one robot may be a more experienced robot.
There are many new difficulties, however, with
regard to knowledge transfer among robots that
develop embodiment-specific knowledge
through individual solo interaction with the
world. This is especially true for heterogeneous
robots, where perceptual and motor capabilities
may differ. In this paper, we propose to
leverage similarity, in the form of a physically
shared context, to learn models of the
differences between two robots. The second
contribution we make is to analyze the cost and
accuracy of several methods for the
establishment of the physically shared context
with respect to such modeling. We demonstrate
the efficacy of the proposed methods in a
simulated domain involving shared attention of
an object.
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Date Issued
2007
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Paper