“This Bot Knows What I’m Talking About!” Human-Inspired Laughter Classification Methods for Adaptive Robotic Comedians

Abstract

Robotic comedians (and social robots generally) need to recognize and adapt to human responses during playful dialog. To support this ability, we determined design guidelines via a survey of 20 human comedians and developed a machine learning pipeline to support comedian-like behaviors by our robotic system. Based on comedian input, we identified that discerning laughter vs. no laughter during a joke setup and big laugh vs. so-so response vs. no laugh after a punchline were important skills for a comedian. To enable these abilities in a robotic system, we used an existing dataset of robot comedy performance audio to train classifiers for audience responses during the setup and after the punchline of jokes. Top-performing models for the above types of discernment performed similarly to human raters who completed the same classification task. Comparison of the current results to our past efforts of a similar nature reveal repeatability of top-performing approaches and generalizability of the approaches to new parts of robot comedy routines. The social intelligence supported by this work can promote the likability and acceptance of robots.