Decades of research into hand-object interaction and manipulation skills has yielded fundamental insights with applications in robotics and motor learning. Nevertheless, integrating visual function (especially binocular function, important to perceive depth) into this equation is crucial, forming a triangle between vision, reaching, and object manipulation. The ReGraD dataset provides kinematic data during hand-object interaction in monocular and binocular conditions at different depths and monocular/binocular conditions. It comprises two sub-datasets: ReGraD A (two measurements) can determine its test-retest reliability, whilst ReGraD B (one measurement) can characterize individuals with and without visual disorders. The ReGraD dataset may aid to (1) gain insights into hand-object interaction under various eye conditions and depths, (2) assess reliability and reproducibility and (3) examine the effects of groups (control vs. patients) and age, among others. The ReGraD dataset contains raw data that can also be used to develop algorithms for data segmentation and data interpolation in the kinematic field. This executable is related to the source code published in GitHub (https://github.com/lorafanda/REGRAD/tree/main/ReGrad_Motor) and can be used in any Windows machine to show the experimenter the different depth conditions.