Title:
Transparent Active Learning for Robots
Transparent Active Learning for Robots
Author(s)
Chao, Crystal
Cakmak, Maya
Thomaz, Andrea L.
Cakmak, Maya
Thomaz, Andrea L.
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Abstract
This research aims to enable robots to learn from
human teachers. Motivated by human social learning, we believe
that a transparent learning process can help guide the human
teacher to provide the most informative instruction. We believe
active learning is an inherently transparent machine learning approach
because the learner formulates queries to the oracle that
reveal information about areas of uncertainty in the underlying
model. In this work, we implement active learning on the Simon
robot in the form of nonverbal gestures that query a human
teacher about a demonstration within the context of a social
dialogue. Our preliminary pilot study data show potential for
transparency through active learning to improve the accuracy
and efficiency of the teaching process. However, our data also
seem to indicate possible undesirable effects from the human
teacher’s perspective regarding balance of the interaction. These
preliminary results argue for control strategies that balance
leading and following during a social learning interaction.
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Date Issued
2010
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Text
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Proceedings
Proceedings