Trust as a design principle in human–robot collaboration: a review of explainable and adaptive control

Human–Robot Collaboration (HRC) has emerged as a fundamental element of new industrial and service systems, wherein humans and robots function within common physical and cognitive environments to achieve shared goals. In addition to traditional issues of safety and productivity, trust has become a critical element influencing cooperation efficiency, human dependence, and sustained system acceptability. This review offers a thorough and reliability-focused summary of HRC research, highlighting the significance of explainable intelligence and adaptive control in promoting trustworthy collaboration. The study initially identifies trust as a fundamental design target in HRC, delineating its dynamic, multifaceted characteristics and its impact on human decision-making and interaction behavior. A systematic review methodology is employed to analyze cutting-edge approaches across essential dimensions, including trust modeling and estimation, multimodal human state and intention recognition, explainable artificial intelligence (XAI) techniques, and adaptive and learning-based control architectures. The analysis emphasizes the role of transparency, interpretability, and context-aware adaptation in establishing trust calibration within safety-critical and dynamic collaborative environments. A cross-sectional synthesis of the literature reveals several critical gaps, including the lack of standardized trust evaluation metrics, limited integration of explainability and control adaptation, inadequate consideration of long-term trust dynamics, and insufficient validation in real-world, unstructured environments. The analysis closes by delineating potential research avenues for cohesive, human-centered HRC frameworks that effortlessly incorporate trust modeling, explainable decision-making, and adaptive control. The ideas offered seek to inform the creation of advanced collaborative robots that are safe, efficient, transparent, adaptive, and trustworthy.