Application of neural networks for self-supervised learning

The learning method of layered neural networks can be supervised or unsupervised. Back propagation learning algorithm is a common method of supervised learning that can learn automatically from teaching patterns. However, accurate teaching patterns are not always available for robotic applications and it is necessary to devise a method of producing them. In this paper, two applications of neural network for self-supervised learning are described. One is a system for which a mobile robot learns its behavior by automatically generating and self- evaluating teaching data through a random walk. The other is a control method of an inverted pendulum using a knowledge-based neural network. The system collects the state data of the inverted pendulum such as angles and angular velocities by trial and error. After that, the system generates teaching data by comparing the collected data with stored knowledge. This knowledge expresses the ideal status of the inverted pendulum when it inverts. The system learns from the generated teaching data and the pendulum inverts stably after some trial and error. In both systems, the neural network learns the teaching data that is generated by the system itself.