A Modular Neural Network Architecture to Learn the Kinematics of Hand Posture During Grasp

The authors propose a modular architecture to learn hand posture kinematics during grasp from little knowledge about the task. The developed model is composed of two parts. The first part is dedicated to the learning of the finger inverse kinematics. This function is fulfilled by a modular architecture (called Fingers Configuration Neural Network, or FCNN) consisting of several neural networks that take into account the discontinuity of the inverse kinematics mapping of the fingers as well as the joint limits. Following the concept of direct associative learning, a second neural network based model is used to optimize the kinematic configuration of the hand according to an evaluative function based on the results of the FCNN. Simulation results show a good learning of grasping postures of various types of objects, with different numbers of fingers and contact sets.