This paper proposes an offline deep Q-network approach to learn when a robot should laugh during dialogue, using engagement estimates from a dyadic speech dataset as rewards, and reports that it beats a fitted Q-iteration baseline.
Data-driven model of nonverbal behavior for socially assistive human-robot interactions,
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Speech Driven Backchannel Generation using Deep Q-Network for Enhancing Engagement in Human-Robot Interaction
This paper proposes an offline deep Q-network approach to learn when a robot should laugh during dialogue, using engagement estimates from a dyadic speech dataset as rewards, and reports that it beats a fitted Q-iteration baseline.