Self-exploration of the Stumpy Robot with Predictive Information Maximization

One of the long-term goals of artificial life research is to create autonomous, self-motivated, and intelligent animats. We study an intrinsic motivation system for behavioral self-exploration based on the maximization of the predictive information using the Stumpy robot, which is the first evaluation of the algorithm on a real robot. The control is organized in a closed-loop fashion with a reactive controller that is subject to fast synaptic dynamics. Even though the available sensors of the robot produce very noisy and peaky signals, the self-exploration algorithm was successful and various emerging behaviors were observed.