Abstract

The dataset contains the data related to human to robot hand mapping, ensuring natural motions and predictability for the operator, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. We exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We consider two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The data are presented in the publication: Meattini, R., Chiaravalli, D., Palli, G., & Melchiorri, C. (2022). Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information. Frontiers in Robotics and AI, 9:878364. doi: 10.3389/frobt.2022.878364