Inverse kinematics learning for robotic arms with fewer degrees of freedom by modular neural network systems

• Main Authors: Oyama, E., Maeda, T., Gan, J.Q., Rosales, E.M., MacDorman, K.F., Tachi, S., Agah, A.
• Format: Conference Proceeding
• Language: English
• Subjects: Arm; Artificial neural networks; Computer networks; Gauss-Newton method; Kinematics; Learning control systems; Least squares methods; Manipulator kinematics; Multi-layer neural network; Neural networks; Newton method; Recursive estimation; Robots
• ISSN: 2153-0858; 2153-0866
• DOI: 10.1109/IROS.2005.1545084

Artificial neural networks have been traditionally employed to learn and compute the inverse kinematics of a robotic arm. However, the inverse kinematics model of a typical robotic arm with joint limits is a multi-valued and discontinuous function. Because it is difficult for a multilayer neural network to approximate this type of function, an accurate inverse kinematics model cannot be obtained by using a single neural network. In order to overcome the difficulties of inverse kinematics learning, we propose a novel modular neural network system that consists of a number of expert modules, where each expert approximates a continuous part of the inverse kinematics function. The proposed system selects one appropriate expert whose output minimizes the expected position/orientation error of the end-effector of the arm. The system can learn a precise inverse kinematics model of a robotic arm with equal or more degrees of freedom than that of its end-effector. However, there are robotic arms with fewer degrees of freedom, where the system cannot learn their precise inverse kinematics model. We have adopted a modified Gauss-Newton method for finding the least-squares solution to address this issue. Through the modifications presented in this paper, the improved modular neural network system can obtain a precise inverse kinematics model of a general robotic arm.