Projecting Robot Intentions Through Visual Cues: Static vs. Dynamic Signaling

TL;DR

This study compares static and dynamic visual signals in human-robot collaboration, demonstrating that combining multiple signals improves task efficiency and reduces cognitive load, with empirical and information-theoretic analysis supporting these findings.

Contribution

It provides empirical evidence and an information-theoretic framework for understanding how different visual signals affect human-robot interaction.

Findings

Combining multiple visual signals enhances task efficiency.

Multiple signals reduce cognitive load on human partners.

Dynamic signals outperform static signals in conveying robot intentions.

Abstract

Augmented and mixed-reality techniques harbor a great potential for improving human-robot collaboration. Visual signals and cues may be projected to a human partner in order to explicitly communicate robot intentions and goals. However, it is unclear what type of signals support such a process and whether signals can be combined without adding additional cognitive stress to the partner. This paper focuses on identifying the effective types of visual signals and quantify their impact through empirical evaluations. In particular, the study compares static and dynamic visual signals within a collaborative object sorting task and assesses their ability to shape human behavior. Furthermore, an information-theoretic analysis is performed to numerically quantify the degree of information transfer between visual signals and human behavior. The results of a human subject experiment show that there are significant advantages to combining multiple visual signals within a single task, i.e., increased task efficiency and reduced cognitive load.