Do Robots Need Body Language? Comparing Communication Modalities for Legible Motion Intent in Human-Shared Spaces

TL;DR

This study evaluates how various signaling methods like motion, lights, text, and audio affect humans' ability to interpret a quadruped robot's navigation intentions in shared spaces.

Contribution

It provides initial evidence comparing implicit expressive motions with explicit signals and their impact on human understanding and trust.

Findings

Expressive motion improves prediction accuracy over other modalities.

Multimodal cues generally enhance interpretability.

Conflicting signals reduce user confidence and trust.

Abstract

Robots in shared spaces often move in ways that are difficult for people to interpret, placing the burden on humans to adapt. High-DoF robots exhibit motion that people read as expressive, intentionally or not, making it important to understand how such cues are perceived. We present an online video study evaluating how different signaling modalities, expressive motion, lights, text, and audio, shape people's ability to understand a quadruped robot's upcoming navigation actions (Boston Dynamics Spot). Across four common scenarios, we measure how each modality influences humans' (1) accuracy in predicting the robot's next navigation action, (2) confidence in that prediction, and (3) trust in the robot to act safely. The study tests how expressive motions compare to explicit channels, whether aligned multimodal cues enhance interpretability, and how conflicting cues affect user confidence and trust. We contribute initial evidence on the relative effectiveness of implicit versus explicit signaling strategies.