Experimental validation of a reach-and grasp optimization algorithm inspired to human arm-hand control

Taking inspiration from neurophysiological studies on synergies in the human grasping action, this paper tries to demonstrate that it is possible to find a general rule for performing a stable, human-like cylindrical grasp with a robotic hand. To this purpose, the theoretical formulation and the experimental validation of a reach-and-grasp algorithm for determining the optimal hand position and the optimal finger configuration for grasping a cylindrical object with known features are presented. The proposed algorithm is based on the minimization of an objective function expressed by the sum of the distances of the hand joints from the object surface. Algorithm effectiveness has preliminarily been tested by means of simulation trials. Experimental trials on a real arm-hand robotic system have then been carried out in order to validate the approach and evaluate algorithm performance.