  In this study, we investigate how a robot can generate novel and creative
actions from its own experience of learning basic actions. Inspired by a
machine learning approach to computational creativity, we propose a dynamic
neural network model that can learn and generate robot's actions. We conducted
a set of simulation experiments with a humanoid robot. The results showed that
the proposed model was able to learn the basic actions and also to generate
novel actions by modulating and combining those learned actions. The analysis
on the neural activities illustrated that the ability to generate creative
actions emerged from the model's nonlinear memory structure self-organized
during training. The results also showed that the different way of learning the
basic actions induced the self-organization of the memory structure with the
different characteristics, resulting in the generation of different levels of
creative actions. Our approach can be utilized in human-robot interaction in
which a user can interactively explore the robot's memory to control its
behavior and also discover other novel actions.
