A model-based strategy for mapping human grasps to robotic hands using synergies

The aim of this paper is to derive the synergies subspace of an anthropomorphic robotic hand using the human hand as a master. A set of grasping postures performed by five subjects in grasping commonly used objects has been mapped to a robotic hand assuming its own kinematics as a simplified model of the human hand. Using an RGB camera and depth sensor for 3D motion capture, the human hand palm pose and fingertip positions have been measured for the reference set of grasping. From the measured fingertip positions a closed-loop inverse kinematics algorithm has been applied to reproduce the joint space configuration of the robotic hand relying on its kinematics, scaled using the human and robotic fingers length ratio. Once the set of grasping has been mapped on the robotic hand, the synergies subspace has been computed applying principal component analysis on the joint configurations. The obtained subspace is tested with experiments on the DEXMART Hand by performing reach to grasp actions on selected objects using the first three predominant synergies. The analysis of these synergies and a comparison with the results on the human hand available in the literature are performed by means of graphical and numerical tools.