Transparent Active Learning for Robots

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.