Point of View: How Perspective Affects Perceived Robot Sociability

TL;DR

This study investigates how different visual perspectives influence human perception of robot sociability and disturbance, revealing that social cues and passing distance significantly impact comfort levels in shared environments.

Contribution

It introduces an immersive VR evaluation method to compare allocentric and egocentric viewpoints, highlighting the importance of perspective in robot social acceptance.

Findings

Allocentric-rated sociable trajectories can seem disturbing from a first-person view.

Passing distance influences perceived disturbance levels.

Head-nod gestures can enhance perceived robot sociability.

Abstract

Ensuring that robot navigation is safe and socially acceptable is crucial for comfortable human-robot interaction in shared environments. However, existing validation methods often rely on a bird's-eye (allocentric) perspective, which fails to capture the subjective first-person experience of pedestrians encountering robots in the real world. In this paper, we address the perceptual gap between allocentric validation and egocentric experience by investigating how different perspectives affect the perceived sociability and disturbance of robot trajectories. Our approach uses an immersive VR environment to evaluate identical robot trajectories across allocentric, egocentric-proximal, and egocentric-distal viewpoints in a user study. We perform this analysis for trajectories generated from two different navigation policies to understand if the observed differences are unique to a single type of trajectory or more generalizable. We further examine whether augmenting a trajectory with a head-nod gesture can bridge the perceptual gap and improve human comfort. Our experiments suggest that trajectories rated as sociable from an allocentric view may be perceived as significantly more disturbing when experienced from a first-person perspective in close proximity. Our results also demonstrate that while passing distance affects perceived disturbance, communicative social signaling, such as a head-nod, can effectively enhance the perceived sociability of the robot's behavior.