  Voluntary behavior of humans appears to be composed of small, elementary
building blocks or behavioral primitives. While this modular organization seems
crucial for the learning of complex motor skills and the flexible adaption of
behavior to new circumstances, the problem of learning meaningful,
compositional abstractions from sensorimotor experiences remains an open
challenge. Here, we introduce a computational learning architecture, termed
surprise-based behavioral modularization into event-predictive structures
(SUBMODES), that explores behavior and identifies the underlying behavioral
units completely from scratch. The SUBMODES architecture bootstraps
sensorimotor exploration using a self-organizing neural controller. While
exploring the behavioral capabilities of its own body, the system learns
modular structures that predict the sensorimotor dynamics and generate the
associated behavior. In line with recent theories of event perception, the
system uses unexpected prediction error signals, i.e., surprise, to detect
transitions between successive behavioral primitives. We show that, when
applied to two robotic systems with completely different body kinematics, the
system manages to learn a variety of complex and realistic behavioral
primitives. Moreover, after initial self-exploration the system can use its
learned predictive models progressively more effectively for invoking model
predictive planning and goal-directed control in different tasks and
environments.
