Predictive action selector for generating meaningful robot behaviour from minimum amount of samples

Our aim is to better understand the action selection process of intelligent systems by looking at their ability of internal prediction. In robotic systems, one problem is to generate meaningful robot behaviour with a very small and simple set of trained motions. An additional problem is to compensate for incomplete sensory data while generating behaviour. We propose a new predictive action selector to contribute to the solution of these problems. Our action selector predicts task-relevant feature and motion sequences, and uses the prediction results to select the robot action. We validate our implemented model on a humanoid robot. The robot generates meaningful behaviour composed out of very simple and few trained motions, and at the same time it compensates for incomplete sensory data such as temporary loss of task-relevant visual features.