Adaptive Robotic Behavior in Industrial Human-Robot Collaboration: A Systematic Review of Taxonomies, Enabling Mechanisms, and Research Frontiers

The inherent variability in human performance introduces stochastic perturbations into manufacturing environments, undermining the seamless coordination required for effective human-robot collaboration (HRC) systems. While human cognitive flexibility enhances adaptability, it simultaneously acts as a source of operational uncertainty, complicating the modeling and optimization of integrated robotic systems. Given these challenges, there is an urgent need to substantially expand the adaptability of robotic systems through real-time detection, algorithmic analysis, and dynamic behavioral adjustments in response to human performance fluctuations. The systematic development of such systems capable of precisely detecting task-specific variations, analyzing them via advanced AI algorithms, and adapting their behavior accordingly remains a critical focus of contemporary research. To evaluate progress in this domain, this study conducts a systematic literature review, synthesizing advancements across 124 publications and identifying underexplored research frontiers. The findings reveal a persistent misalignment between current technical capabilities and the requirements of adaptive collaboration in dynamic industrial environments. Key gaps include the absence of explainable AI frameworks for transparent decision-making, limited generalizability of adaptive control architectures, and a lack of proactive strategies that anticipate rather than merely react to performance deviations.