Abstract
As social robots increasingly enter people’s lives, coherence of personality is an important challenge for long-term human-robot interactions. We extend an architecture that acquires dialog through crowdsourcing to author both verbal and non-verbal indicators of personality. We demonstrate the efficacy of the approach through a four-day study in which teams of participants interacted with a social robot expressing one of two personalities as the host of a competitive game. Results indicate that the system is able to elicit personality-driven language behaviors from the crowd in an incremental and ongoing way and produce a coherent expression of that personality during face-to-face interactions over time.
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