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2017
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(2017) : Acquiring social interaction behaviours for telepresence robots via deep learning from demonstration: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): Vancouver, British Columbia, Canada: IEEE, S. 37-42
DOI: https://doi.org/10.1109/IROS.2017.8202135 Abstract: As robots begin to inhabit public and social spaces, it is increasingly important to ensure that they behave in a socially appropriate way. However, manually coding social behaviours is prohibitively difficult since social norms are hard to quantify. Therefore, learning from demonstration (LfD), wherein control policies are inferred from demonstrations of correct behaviour, is a powerful tool for helping robots acquire social intelligence. In this paper, we propose a deep learning approach to learning social behaviours from demonstration. We apply this method to two challenging social tasks for a semi-autonomous telepresence robot. Our results show that our approach outperforms gradient boosting regression and performs well against a hard-coded controller. Furthermore, ablation experiments confirm that each element of our method is essential to its success.
Keywords: ablation experiments, challenging social tasks, Cloning, control engineering computing, correct behaviour, deep learning approach, deep learning from demonstration, gradient boosting regression, gradient methods, hard-coded controller, human-robot interaction, ieee xplore, Künstliche Intelligenz, learning (artificial intelligence), LfD, machine learning, public spaces, Regression Analysis, Robot sensing systems, semiautonomous telepresence robot, social behaviour, Social intelligence, social interaction behaviours, Social Norms, social spaces, telepresence robots
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