Visual Proactivity: Enhancing Human-Robot Collaboration through Intent Communication
Valerio Bo, Edison Bejarano, Anais Garrell, Alberto Sanfeliu
AI summary
Problem
Most human-robot collaboration research focuses on how robots infer human intent, leaving the inverse problem of how robots can non-verbally communicate their own intent to guide human decisions largely unexplored.
Approach
The authors introduce visual proactivity, a method where a robot uses its planned trajectory and physical motion to subtly signal its intended goals, actively influencing human partners to align their actions toward optimal outcomes.
Key results
- Developed a proactive behavior that conveys robot intentions through visual motion feedback
- Validated via user study that humans can distinguish reactive, anticipatory, and proactive robot behaviors
- Demonstrated that visual proactivity improves alignment, coordination, and predictability in handover tasks
- Confirmed humans accurately interpret non-verbal visual cues without explicit verbal communication
Why it matters
This hardware-free signaling method enables robots to actively shape human decision-making, paving the way for more intuitive and efficient collaborative systems.
Abstract
As robots transition from performing repetitive tasks to collaborating with humans, understanding human intent becomes crucial to effective interaction. Anticipation enables robots to predict human actions, while proactivity allows them to take initiative and guide human behavior toward optimal outcomes. Although research has largely focused on how robots infer and respond to human intentions, less attention has been paid to how robots communicate their own intent. This paper introduces visual proactivity, a novel, simple yet effective approach that enables robots to communicate their intentions through visual feedback, influencing human behavior and enhancing transparency and fluency. We develop and eval- uate proactive robotic behaviors in a human-to-robot handover scenario, where a user study validates human perception of reactive, anticipatory, and proactive behaviors. The results demonstrate that effective visual proactivity fosters better alignment and coordination, paving the way for more intuitive human-robot collaboration.