Exploring Machine-like Behaviors for a Socially Acceptable Robot Navigation in Elevators
Danilo Gallo , Shreepriya Gonzalez-Jimenez , Maria Antonietta Grasso, Cecile Boulard, Tommaso Colombino
HRI 2022
Session: Norms and Biases
Abstract
In this paper, we present our ongoing research on socially acceptable robot navigation for an indoor elevator sharing scenario. Informed by naturalistic observations of human elevator use, we discuss the social nuances involved in a seemingly simple activity like taking an elevator and the challenges and limitations of modeling robot behaviors based on a full humanlike approach. We propose the principle of machine-like for the design of robot behavior policies that effectively accomplish tasks without being disruptive to the routines of people sharing the elevator with the robots. We explored this approach in a bodystorming session and conducted a preliminary evaluation of the resulting considerations through an online user study. Participants differentiated robots from humans for issues of proxemics and priority, and machine-like behaviors were preferred over human-like behaviors. We present our findings and discuss the advantages and limitations identified for both approaches for designing socially acceptable navigation behaviors.
WEB:: https://humanrobotinteraction.org/2022/
Videos for HRI 2022