A Robot Walks into a Bar: Automatic Robot Joke Success Assessment

Effective social robots should leverage humor’s unique ability to improve relationship connections and dispel stress, but current robots possess limited (if any) humorous abilities. In this paper, we aim to supplement one aspect of autonomous robots by giving robotic systems the ability to "read the room" to assess how their humorous statements are received by nearby people in real time. Using a dataset of the audio of crowd responses to a robotic comedian over multiple performances (first presented in past work), we establish human-labeled joke success ground truths and compare individual human rater accuracy against the outputs of lightweight Machine Learning (ML) approaches that are easy to deploy in real-time joke assessment. Our results indicate that all three ML approaches (naive Bayes, support vector machines, and single-hidden-layer feedforward neural networks) performed significantly better than the baseline approach used in our past work. In particular, support vector machines and neural network approaches are comparable to a human rater in the task of assessing if a joke failed or not in certain cases. The products of this work will inform self-assessment techniques for robots and help social robotics researchers test their own assessment methods on realistic data from human crowds.