Active Shadowing (ASD): Manipulating Perception of Robotic Behaviors via Implicit Virtual Communication

TL;DR

This paper introduces Active Shadowing (ASD), a novel implicit visual communication method using shadows via augmented reality, which influences perception of robotic behaviors without altering physical actions, maintaining optimality and enhancing understandability.

Contribution

The work presents ASD as a new implicit communication technique that creates perceptual illusions to influence robot perception without changing physical behavior, outperforming traditional explicit methods.

Findings

ASD effectively creates illusions that preserve robot optimality.

ASD enhances understandability compared to explicit communication.

Effectiveness of ASD varies under different conditions.

Abstract

Explicit communication is often valued for its directness in presenting information but requires attention during exchange, resulting in cognitive interruptions. On the other hand, implicit communication contributes to tacit and smooth interaction, making it more suitable for teaming, but requires inference for interpretation. This paper studies a novel type of implicit visual communication (IVC) using shadows via visual projection with augmented reality, referred to as active shadowing (ASD). Prior IVC methods, such as legible motion, are often used to influence the perception of robot behavior to make it more understandable. They often require changing the physical robot behavior, resulting in suboptimality. In our work, we investigate how ASD can be used to achieve similar effects without losing optimality. Our evaluations with user studies demonstrates that ASD can effectively creates ''illusions'' that maintain optimal physical behavior without compromising its understandability. We also show that ASD can be more informative than other explicit communication methods, and examine the conditions under which ASD becomes less effective.