Precision grasp synergies for dexterous robotic hands

Abstract

In this paper we present a method for the generation of hand postural synergies for different precision grasp types to be used in dextrous robot hands. Our method records the robot hand motions while teleoperated by human subjects via a dataglove, doing different grasp types on a series of objects. This exploits the fact that humans automatically compensate for calibration errors on the glove to robot mapping. The method is applied to the Shadow Robot Hand and to the iCub Hand. Despite the significantly different kinematics and different number and mechanism of actuators, the analysis results in useful postural synergies for both hands. The effective number of degrees-of-freedom to reproduce the recorded variance using the synergies is shown to be 2..6 for the different grasps, corresponding to a massive reduction of grasp-search complexity. Therefore, the existing actuators are enough to drive the hands with realistic human-like postures and in-hand movements. While previous work on synergies mostly concentrated on static grasping and power-grasps, our work confirms that human precision grasps and manipulation motions also lie on low-dimensional spaces.