A Hybrid Dataset for Studying Human Trust Dynamics in Sequential Human-Robot Collaboration

Version 1.1 Update Notes Modality Field Alignment: The modality field has been updated to ensure full consistency with the terminology and classification used in the corresponding research paper. Floating-point Precision Refinement: Addressed an issue in the previous version where certain numerical fields displayed excessive decimal places. This was a technical artifact arising from the data serialization process (floating-point precision representation). We have now applied consistent rounding to these values to improve readability and conform to the data's physical significance, without altering the underlying measurement accuracy. This dataset, A Dataset for Trust Dynamics in Human–Robot Collaboration, provides multi-modal recordings and annotations of human trust evolution during sequential collaborative tasks. The data were collected through a unified experimental framework combining three modalities: (1) virtual human-in-the-loop experiments in VR environments, (2) large language model (LLM)–based human simulation, and (3) real-world quadruped robot collaboration experiments. Each trial captures the temporal evolution of human trust alongside task state, observations, robot recommendations, human decisions, and rewards, forming a complete trajectory of trust dynamics. The dataset includes 10 sequential subtasks per trial. Key variables include: state — true environment state per timestep. robot_observation — human observation under uncertainty. trust — normalized human trust value . robot_action — decisions made by robot. human_action — decisions made by human. task_result — task feedback. The dataset is designed for research in trust prediction, human–robot collaboration modeling, and trust-aware reinforcement learning.All data are anonymized and formatted as JSON and CSV files for easy processing. A detailed README and schema description are included in the release package. Usage Notes:Researchers are encouraged to use this dataset for model development, benchmarking, and evaluation of trust-aware AI systems. Please cite this dataset as indicated below when used in publications.