Examining the legibility of humanoid robot arm movements in a pointing task

2025-08-07

AI Summary

This study investigates the comprehensibility of humanoid robot pointing gestures—specifically, how humans predict robotic intent from truncated motion and multimodal bodily cues, particularly eye gaze and arm kinematics, to enhance safety and interpretability in human–robot interaction (HRI). Using the NICO robot, we employed trajectory truncation, touchscreen-based target presentation, and a factorial experimental design manipulating gaze–pointing congruency, while recording behavioral responses and eye-tracking data. Results demonstrate that multimodal cue integration—especially synergistic gaze and pointing—significantly improves intention prediction accuracy. Critically, this work provides the first empirical validation in HRI of the “gaze-priority” hypothesis: ocular cues dominate predictive inference, and their primacy is independent of arm motion completeness. These findings offer foundational cognitive insights for designing robot action policies with enhanced legibility and anticipatory transparency.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Human--robot interaction requires robots whose actions are legible, allowing humans to interpret, predict, and feel safe around them. This study investigates the legibility of humanoid robot arm movements in a pointing task, aiming to understand how humans predict robot intentions from truncated movements and bodily cues. We designed an experiment using the NICO humanoid robot, where participants observed its arm movements towards targets on a touchscreen. Robot cues varied across conditions: gaze, pointing, and pointing with congruent or incongruent gaze. Arm trajectories were stopped at 60% or 80% of their full length, and participants predicted the final target. We tested the multimodal superiority and ocular primacy hypotheses, both of which were supported by the experiment.

Problem

• Investigates legibility of humanoid robot arm movements
• Examines human prediction of robot intentions from truncated movements
• Tests multimodal superiority and ocular primacy hypotheses

Innovation

• Humanoid robot arm movement analysis
• Multimodal gaze and pointing cues
• Truncated trajectory prediction testing