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

TL;DR

Active Shadowing (ASD) introduces implicit visual communication by projecting realistic virtual shadows in augmented reality to influence the perceived robot trajectory without changing the physical trajectory $\gamma^-$. It merges $\gamma^-$ with a shadow-derived cue $\tau^* = S(\gamma^*)$ to form $\gamma^D = \gamma^- + \tau^*$, creating a perceptual illusion $\Sigma^*$ while maintaining cost optimality. Across three perceptual tasks (illusion of motion, legible motion, imminent collision) and a large user study, ASD demonstrates strong perceptual influence, competitive or superior informativeness relative to explicit cues, and generally comparable mental workload, with the caveat that maintaining the shadow–robot association is crucial. This positions ASD as a practical, low-attention implicit communication technique that complements existing explicit and implicit methods and can extend to other non-verbal channels in HRI.

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.

Figures (12)

• Figure 1: Scenario that motivates the need for constant foreshadowing of the robot's behavior. In ASD, this is achieved by using virtual shadows to continuously project the robot's future state to assist in maintaining safer interaction.
• Figure 2: Illustration of the differences between ASD (right) and EC (left) and other IVC methods (directly changing $\gamma^-$).
• Figure 3: (a-b) Manipulating the perception of behavior via ASD. (c-d) Manipulating the perception of behavior using EC via projecting the hologram of the robot at a different position. In both cases, the robot did not move but the IC method provides a stronger illusion of motion.
• Figure 4: Geometric representation of elevation/pitch ($\alpha$) and azimuth/yaw ($\beta$) of light source and their relation to shadow.
• Figure 5: Flowchart of ASD