1P1-E07 Learning Android Control using Growing Neural Networks
Heni Ben Amor, Shuhei Ikemoto, Takashi Minato, Hiroshi Ishiguro · The Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) · 2006
Fixed sized neural networks are a popular technique for learning the adaptive control of non-linear plants. When applied to the complex control of android robots, however, they suffer from serious limitations, such as the moving target problem i.e. the interference between old and newly learned knowledge. To overcome these problems, we propose the use of growing neural networks in a new learning framework based on the process of consolidation. The new framework is able to overcome the drawbacks of sigmoidal neural networks, while maintaining their power of generalization. In experiments the framework was successfully applied to the control of an android robot.