Geppetto: Enabling Semantic Design of Expressive Robot Behaviors
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.
BibTeX
@conference{Desai-2019-113388,author = {Ruta Desai and Fraser Anderson and Justin Matejka and Stelian Coros and James McCann and George W. Fitzmaurice and Tovi Grossman},
title = {Geppetto: Enabling Semantic Design of Expressive Robot Behaviors},
booktitle = {Proceedings of CHI Conference on Human Factors in Computing Systems (CHI '19)},
year = {2019},
month = {May},
}