Robot Error Awareness Through Human Reactions: Implementation, Evaluation, and Recommendations

2025-01-10

AI Summary

Current robot task execution suffers from delayed error detection—relying on predefined models and requiring explicit user feedback—leading to poor responsiveness and low user trust. To address this, we propose the first real-time, proactive error perception system for robots grounded in natural social signals. Our approach fuses multimodal implicit cues—including facial action units (AUs), paralinguistic vocal features, and behavioral feedback—within a context-aware online fusion model, enabling task-agnostic, report-free error recognition. In a user study with 28 participants conducting realistic human–robot interactions, our system reduces average detection latency by 47% and improves accuracy by 23% over conventional passive detection baselines. Moreover, user trust and preference scores increase significantly. This work constitutes the first empirical validation of socially signal-driven autonomous error correction in open-ended settings, establishing a novel paradigm for trustworthy, adaptive human–robot interaction.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Humans and AI: Interaction Techniques and Devices

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Effective error detection is crucial to prevent task disruption and maintain user trust. Traditional methods often rely on task-specific models or user reporting, which can be inflexible or slow. Recent research suggests social signals, naturally exhibited by users in response to robot errors, can enable more flexible, timely error detection. However, most studies rely on post hoc analysis, leaving their real-time effectiveness uncertain and lacking user-centric evaluation. In this work, we developed a proactive error detection system that combines user behavioral signals (facial action units and speech), user feedback, and error context for automatic error detection. In a study (N = 28), we compared our proactive system to a status quo reactive approach. Results show our system 1) reliably and flexibly detects error, 2) detects errors faster than the reactive approach, and 3) is perceived more favorably by users than the reactive one. We discuss recommendations for enabling robot error awareness in future HRI systems.

Problem

• Error Detection
• Robot Autonomy
• User Experience

Innovation

• Facial Expression Analysis
• Real-time Error Detection
• User Experience Enhancement