In this summary of previous work, I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively more “beautiful.” Curiosity is the desire to create or discover more non-random, non-arbitrary, “truly novel,” regular data that allows for compression progress because its regularity was not yet known. This drive maximizes “interestingness,” the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and recent artificial systems.

Recent advances in robotics lead us to consider, on the one hand, the notion of a kernel, a set of stable algorithms that drive developmental dynamics and, on the other hand, variable body envelopes that change over time. This division reverses the classic notion of a fixed body on which different software can be applied to consider a fixed software that can be applied to different kinds of embodiment. We discuss the case of generic algorithms capable of learning to control a robotic body without knowing its characteristics beforehand. With such kind of algorithms you can perform experiences where you can precisely characterize the importance of the embodiment in the final behavior obtained, simply by changing the embodiment and keeping the software kernel stable. Thus, it becomes possible to study how a particular embodiment shapes developmental trajectories in specific ways. It also leads us to a new view of the development of skills, from sensorimotor dexterity to abstract thought, based on the notion of a fluid body in continuous redefinition.