Flexible and robust robotic arm design and skill learning by using recurrent neural networks

It is undeniable that the ability to grasp and handle an object is vital for service robots. From object recognition to object grasping motion, the motion execution should be as fast as possible. Due to the possible position variation of the target object to be grasped, online planning of grasping motion should be done. In order to achieve flexible grasping motion, recurrent neural network could be implemented as an alternative to conventional manipulation method which is based on kinematic and dynamic analysis. However, the application of recurrent neural network model requires good and easily obtainable training data. Hence, a novel robotic arm design with high flexibility is proposed to facilitate the training and implementation of the recurrent neural network model. The feasibility of the proposed robotic arm design is evaluated via the training, learning and testing of stochastic continuous time recurrent neural network (S-CTRNN) model with grasping a box motion.