A Novel Skill Modeling Approach: Integrating Vergnaud's Scheme with Cognitive Architectures

2025-09-16

AI Summary

Modeling and transferring human operator skills—particularly in dynamic tasks such as welding—remains a critical challenge in human–robot collaboration. Method: This study proposes a cognitive-constrained, cross-domain skill representation framework. It innovatively embeds Vergnaud’s schema theory into a cognitive architecture, explicitly incorporating constraints including temporal delays, limited cognitive resources, and concurrent multitasking. Skill structures are formalized using predicate logic, and the perception–decision–action closed loop—along with automatic behavior activation—is systematically modeled. Contribution/Results: The work establishes the first bi-directional bio-mechanical skill transfer model capable of real-time adaptive simulation. It enables comparable performance analysis between unassisted and robot-assisted operation scenarios. By grounding skill modeling in empirically validated cognitive principles, the framework provides a theoretically rigorous foundation and a reusable paradigm for designing and evaluating human–robot collaborative systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Intelligent Robots: Cognitive Robotics
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

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

Abstract

Human-machine interaction is increasingly important in industry, and this trend will only intensify with the rise of Industry 5.0. Human operators have skills that need to be adapted when using machines to achieve the best results. It is crucial to highlight the operator's skills and understand how they use and adapt them [18]. A rigorous description of these skills is necessary to compare performance with and without robot assistance. Predicate logic, used by Vergnaud within Piaget's scheme concept, offers a promising approach. However, this theory doesn't account for cognitive system constraints, such as the timing of actions, the limitation of cognitive resources, the parallelization of tasks, or the activation of automatic gestures contrary to optimal knowledge. Integrating these constraints is essential for representing agent skills understanding skill transfer between biological and mechanical structures. Cognitive architectures models [2] address these needs by describing cognitive structure and can be combined with the scheme for mutual benefit. Welding provides a relevant case study, as it highlights the challenges faced by operators, even highly skilled ones. Welding's complexity stems from the need for constant skill adaptation to variable parameters like part position and process. This adaptation is crucial, as weld quality, a key factor, is only assessed afterward via destructive testing. Thus, the welder is confronted with a complex perception-decision-action cycle, where the evaluation of the impact of his actions is delayed and where errors are definitive. This dynamic underscores the importance of understanding and modeling the skills of operators.

Problem

• Modeling human operator skills for human-machine interaction
• Integrating cognitive constraints with Vergnaud's scheme theory
• Understanding skill adaptation in complex welding operations

Innovation

• Integrating Vergnaud's scheme with cognitive architectures
• Modeling operator skills with cognitive constraints
• Addressing delayed feedback in welding operations