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Geppetto: Enabling Semantic Design of Expressive Robot Behaviours

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

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

Geppetto: Enabling Semantic Design of Expressive Robot Behaviours (6:04 min.)

Video title (x:xx min.)

Abstract

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.

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