Artificial curiosity for autonomous space exploration

Curiosity is an essential driving force for science as well as technology, and has led mankind to explore its surroundings, all the way to our current understanding of the universe. Space science and exploration is at the pinnacle of each of these developments, in that it requires the most advanced technology, explores our world and outer space, and constantly pushes the frontier of scientific knowledge. Manned space missions carry disproportionate costs and risks, so it is only natural for the field to strive for autonomous exploration. While recent innovations in engineering, robotics and AI provide solutions to many sub-problems of autonomous exploration, insufficient emphasis has been placed on the higher level question of autonomously deciding what to explore. Artificial curiosity, the subject of this paper, precisely addresses this issue. We will introduce formal notions of “interestingness” based on the concepts of (1) compression progress through discovery of novel regularities in the observations, and (2) coherence progress through selection of data that “fits” the already known data in a compression-based way. Further, we discuss how to construct a system that exhibits curiosity driven by the interestingness of certain types of novel observations, with the mission to curiously go where no probe has gone before