Mapping Grasps from the Human Hand to the DEXMART Hand by means of Postural Synergies and Vision

This work aims at defining a suitable postural synergies subspace for the DEXMART Hand from observation of human hand grasping postures. Previous works were carried out on a preliminary prototype (the UB Hand IV), without neither proprioceptive integrated sensors nor external sensors, by means of a joint-to-joint mapping technique. Using an RGB camera and depth sensor for 3D motion capture, the human hand palm pose and fingertip positions have been measured for a reference set of grasping postures. The proposed method for the determination of the synergies subspace is based on the kinematics mapping from the human hand to the robotic hand using data from experiments involving five subjects. The subjects’ hand configurations have been mapped to the robotic hand by matching the hand pose and fingertip positions and applying a closed-loop inverse kinematic algorithm. Suitable scaling factors have been used to adapt the DEXMART Hand kinematics to the subjects’ hand dimension. By means of Principal Component Analysis (PCA), the kinematic patterns of the first three predominant synergies have been computed and a brief comparison with the previous method and kinematics is reported. Finally, a synergy-based control strategy has been used for testing the efficiency of the grasp synthesis method