Abstract
We humans learn behavior skills through repetitions of own behavior experiences. In the brain science perspective, we speculate such skilled behaviors might be acquired in inferior parietal lobe through the repeated sensory-motor experiences and propose a brain model. We utilized two neural networks. One is a Continuous Time Recurrent Nenral Network (CTRNN) which learns the trajectory of joint angles to achieve the task. Another is Recurrent Neural Network (RNN) which learns the relation of muscle activity to joint angles. To test the proposed model, we applied it to the task of reaching for an object using real robot with a tendon-based actuation mechanism.
