An Adaptive Neural Network Learning-based Solution for the Inverse Kinematics of Humanoid Fingers

摘要

This paper presents an adaptive neural network learning-based solution for the inverse kinematics of humanoid fingers. For the purpose, we specify an effective finger model by considering the interphalangeal joint coordination inherent in human fingers. In order to find a proper joint combination for any fingertip trajectory, we propose an adaptive learning scheme by using a multi-layered neural network. It is interesting to use an adaptive learning rate algorithm that leads the neural network to get the inverse kinematic solution quickly. The usefulness of the proposed approach is verified by exemplary simulations for the general motion of humanoid fingers.