Geppetto

Abstract

Geppetto: Enabling Semantic Design of Expressive Robot Behaviours

Ruta Desai, Fraser Anderson, Justin Matejka, Stelian Coros, James McCann, George Fitzmaurice, Tovi Grossman

ACM SIGCHI Conference on Human Factors in Computing Systems 2019 (Best Paper Award – Top 1%)

Expressive robots are useful in many contexts, from industrial to entertainment applications. However, designing expressive robot behaviors requires editing a large number of unintuitive control parameters. We present an interactive, data-driven system that allows editing of these complex parameters in a semantic space. Our system combines a physics-based simulation that captures the robot’s motion capabilities, and a crowd-powered framework that extracts relationships between the robot’s motion parameters and the desired semantic behavior. These relationships enable mixed-initiative exploration of possible robot motions. We specifically demonstrate our system in the context of designing emotionally expressive behaviors. A user-study finds the system to be useful for more quickly developing desirable robot behaviors, compared to manual parameter editing.