Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment

This paper investigates how behavior primitives are self-organized in a neural network model utilizing a distributed representation scheme. The model is characterized by so-called parametric biases which adaptively modulate the encoding of different behavior patterns in a single recurrent neural net (RNN). Our experiments, using a real robot arm, showed that a set of end-point and oscillatory behavior patterns are learned by self-organizing fixed points and limit cycle dynamics that form behavior primitives. It was also found that diverse novel behavior patterns can be generated by modulating the parametric biases arbitrarily. Our analysis showed that such diversity in behavior generation emerges because a nonlinear map is self-organized between the space of parametric biases and that of the behavior patterns. The origin of the observed nonlinearity from the distributed representation is discussed.