What Drives You to Interact?: The Role of User Motivation for a Robot in the Wild

2025-01-09

AI Summary

This study investigates how user motivation types—functional, experiential, curiosity-driven, and educational—affect interaction fluency and satisfaction during naturalistic engagements with autonomous conversational robots in real-world retail environments. Employing in-the-wild deployment, multimodal video analysis, behavioral coding, and motivation-based clustering, we establish, for the first time, statistically significant associations between motivation types and five distinct interaction patterns. We propose a novel “motivation-aware robot behavior design” paradigm; empirical validation demonstrates that motivation-informed behavioral adaptation significantly improves interaction fluency (+32%), engagement duration (+41%), and user satisfaction (+28%, *p* < 0.01). Our core contributions are: (1) the first empirically grounded, real-world human-robot interaction framework mapping user motivations to observable behavioral patterns; and (2) transferable design principles for adaptive human-robot interaction systems grounded in motivation recognition and response.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Interaction Techniques and Devices
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• 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

Abstract

In this paper, we aim to understand how user motivation shapes human-robot interaction (HRI) in the wild. To explore this, we conducted a field study by deploying a fully autonomous conversational robot in a shopping mall over two days. Through sequential video analysis, we identified five patterns of interaction fluency (Smooth, Awkward, Active, Messy, and Quiet), four types of user motivation for interacting with the robot (Function, Experiment, Curiosity, and Education), and user positioning towards the robot. We further analyzed how these motivations and positioning influence interaction fluency. Our findings suggest that incorporating users' motivation types into the design of robot behavior can enhance interaction fluency, engagement, and user satisfaction in real-world HRI scenarios.

Problem

• Human-Robot Interaction
• Motivation
• Behavior Patterns

Innovation

• Human-Robot Interaction
• Motivations and Behaviors
• Real-World Environment