Geppetto: Enabling Semantic Design of Expressive Robot Behaviors

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

January 2019 · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI)

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.

DOI PDF

Figures

* Figure 1: Overview of the semantic motion design framework: It consists of four main building blocks-- (a) a dataset of parameterized expressive robot motions, (b) a crowdsourcing set-up for estimating the emotional perception of motions in the dataset, (c) regression analysis for establishing relationships between motion parameters and the emotional perception of the resultant motion, and (d) an intuitive design tool backed by these data-driven parameter-emotion relationships.

Figure 2: Users can design expressive motions for two distinct types of robots: (a) a quadruped, and (b) a robotic arm, while exploring the space of possible motions.

Figure 3: User interface overview. The 3D preview window renders the robot's motion. The gallery and annotated sliders provide semantically relevant information at design time.

Figure 4: An example workflow designing an angry robot.

Figure 5: UI elements. (a) Parameter information is displayed as tooltips, and highlighted directly on the robot. (b) Parameter-emotion perception curve (in red) is visualized with an uncertainty band

Figure 6: (a) The quadruped’s motion is parameterized using joint poses, walking speed, foot height, gait time, and gait pattern (shown in red). (b) The arm is driven by a Boids flocking simulation. T

Figure 7: Interface crowd-workers used to judge emotion.

Figure 8: Comparison of predicted emotion values (orange) with their crowdsourced values (gray) for the test samples of the quadruped motion dataset. The best (happy) and worst (surprised) fitting emotion categories are displayed.

Figure 8: Interfaces used in the study two conditions, parameter (left) and semantic (right).

Figure 9: Mean emotion perception scores of the top 5 designs from the original dataset (Synthesized) with those created by the study participants. Bars show 95% CIs.

Figure 10: Individual and average design times are shown using dots and lines respectively, for both of our UIs. Shaded regions represent 95% CI.

Figure 11: The evolution of the quality of user-designs (bars represent 95% CIs at each time step). The dotted lines represent the linear fit of mean scores over all emotions and participants, and the bands are a 95% CI around the fit.

BibTeX

@inproceedings{10.1145/3290605.3300599,
 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.},
 address = {New York, NY, USA},
 author = {Desai, Ruta and Anderson, Fraser and Matejka, Justin and Coros, Stelian and McCann, James and Fitzmaurice, George and Grossman, Tovi},
 booktitle = {Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems},
 doi = {10.1145/3290605.3300599},
 isbn = {9781450359702},
 keywords = {expressive robots, semantic editing, robots, semantic design},
 location = {Glasgow, Scotland Uk},
 numpages = {14},
 pages = {1–14},
 publisher = {Association for Computing Machinery},
 series = {CHI '19},
 title = {Geppetto: Enabling Semantic Design of Expressive Robot Behaviors},
 url = {https://doi.org/10.1145/3290605.3300599},
 year = {2019}
}