Shape oriented object recognition on grasp using features from enclosure based exploratory procedure

Abstract

The potential of humans to recognize known objects while grasping, without the help of vision, is an exciting supposition to the robotics community. With a focus on reproducing such a natural aptitude in prosthetic hands, this paper reports a kinematic approach to exploring the human hand’s object recognition functionality during a grasp. Finger kinematics vary while grasping objects of different shapes and sizes. The authors emphasized learning the variations while grasping different objects through a forward kinematics model of the human hand. Finger joint kinematics for objects of two specific shape categories: spherical and cylindrical, were recorded during grasping experiments using a customized data glove to deduce the fingertip coordinates. An algorithm has been developed to derive novel three-dimensional grasp polyhedrons from fingertip coordinates. Areas of these polyhedrons and finger kinematics have been used as features to train classification algorithms. Comparing the recognition results using only finger kinematics as features revealed that the inclusion of the shape primitives increases the accuracies of the classifiers by 2–6% while recognizing the objects. This work analytically confirms that finger kinematics and the object’s shape primitives are vital information for visionless object recognition.