Trust plays a central role in human–robot interaction, as it shapes how humans rely on and collaborate with autonomous systems under uncertainty. Existing approaches often treat trust as a static value or rely on highly data-driven models that lack interpretability and require extensive amounts of context-specific datasets. This limits their applicability in simulation and control. Human trust is modeled as a latent, time-varying inter nal state and is formulated as an end-to-end identification problem grounded in empirical interaction data. A modular, deterministic dynamic model integrates dispositional, situational, and learned components within a discrete-time framework, capturing both stable predispositions and interaction-driven adaptation. A human subject experimental paradigm including 35 participants elicits informative trust dynamics and enables pa rameter identification and evaluation in terms of fit, generalization, and robustness. The resulting model remains competitive against validated baselines in reproducing observed trust trajectories while maintaining interpretability, modularity, and practical identifiability. Closed-loop coupling with probabilistic decision models examines the relationship between inferred trust and observable behavior, showing that trust is behaviorally relevant but insufficient on its own to explain human actions. The resulting framework enables executable, empirically grounded modeling of trust dynamics in human–robot interaction and supports the development of trust-aware simulation environments and human-centered robotic systems. ... Read more