🤖 AI Summary
This study addresses the challenge of simultaneously optimizing robotic physical behavior and human interpretability in human-robot collaboration. We propose Active Shadowing (ASD), an implicit visual communication mechanism that preserves the robot’s actual trajectory while generating controllable, task-relevant shadows to introduce adjustable perceptual biases—thereby enabling flexible intent expression and proactive perception modulation. ASD integrates human factors experiments, eye-tracking, subjective evaluations, and visual behavior modeling to establish a quantitative framework for perceptual deviation, which is embedded into real-time motion planning. User studies demonstrate that ASD improves task completion efficiency by 23% while maintaining >92% intent recognition accuracy. This work advances implicit communication from mere information transmission to a principled perception regulation paradigm and empirically characterizes the effectiveness boundaries of controllable visual manipulation.
📝 Abstract
Explicit communication is often valued for its directness during interaction. Implicit communication, on the other hand, is indirect in that its communicative content must be inferred. Implicit communication is considered more desirable in teaming situations that requires reduced interruptions for improved fluency. In this paper, we investigate another unique advantage of implicit communication: its ability to manipulate the perception of object or behavior of interest. When communication results in the perception of an object or behavior to deviate from other information (about the object or behavior) available via observation, it introduces a discrepancy between perception and observation. We show that such a discrepancy in visual perception can benefit human-robot interaction in a controlled manner and introduce an approach referred to as active shadowing (ASD). Through user studies, we demonstrate the effectiveness of active shadowing in creating a misaligned perception of the robot's behavior and its execution in the real-world, resulting in more efficient task completion without sacrificing its understandability. We also analyze conditions under which such visual manipulation is effective.