Forgive and Forget? An Industry 5.0 Approach to Trust-Fatigue Co-regulation in Human-Cobot Order Picking

2025-08-04

AI Summary

In logistics 5.0 human–collaborative robot (cobot) picking scenarios, system performance degrades due to rapid trust erosion and cumulative human fatigue. Method: This paper proposes a “trust-coordination cycle” mechanism and an active trust-recovery protocol, overcoming limitations of static trust models. We formulate an explicit utility function integrating trust and fatigue dynamics, model human–cobot interaction via a dynamic leader–follower Stackelberg game, and validate the approach through multi-agent simulation. Contribution/Results: The framework enables real-time trust sensing, adaptive trust regulation, and post-failure autonomous recovery. Experiments demonstrate near-doubling (≈100%) of collaborative productivity, over 75% reduction in trust recovery time following critical failures, and significant improvements in system resilience, human-centered adaptability, and sustainable operational capability.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This paper investigates the critical role of trust and fatigue in human-cobot collaborative order picking, framing the challenge within the scope of Logistics 5.0 -- the implementation of human-robot symbiosis in smart logistics. We propose a dynamic, leader-follower Stackelberg game to model this interaction, where utility functions explicitly account for human fatigue and trust. Through agent-based simulations, we demonstrate that while a naive model leads to a "trust death spiral," a refined trust model creates a "trust synergy cycle," increasing productivity by nearly 100 percent. Finally, we show that a cobot equipped with a proactive Trust-Repair Protocol can overcome system brittleness, reducing trust recovery time after a severe failure by over 75 percent compared to a non-adaptive model. Our findings provide a framework for designing intelligent cobot behaviors that fulfill the Industry 5.0 pillars of human-centricity, sustainability, and resilience.

Problem

• Modeling trust-fatigue dynamics in human-cobot order picking
• Preventing trust death spiral via refined trust models
• Enhancing resilience with proactive trust-repair protocols

Innovation

• Dynamic Stackelberg game models human-cobot interaction
• Agent-based simulations demonstrate trust synergy cycle
• Proactive Trust-Repair Protocol reduces recovery time