  Legged robots have significant potential to operate in highly unstructured
environments. The design of locomotion control is, however, still challenging.
Currently, controllers must be either manually designed for specific robots and
tasks, or automatically designed via machine learning methods that require long
training times and yield large opaque controllers. Drawing inspiration from
animal locomotion, we propose a simple yet versatile modular neural control
structure with fast learning. The key advantages of our approach are that
behavior-specific control modules can be added incrementally to obtain
increasingly complex emergent locomotion behaviors, and that neural connections
interfacing with existing modules can be quickly and automatically learned. In
a series of experiments, we show how eight modules can be quickly learned and
added to a base control module to obtain emergent adaptive behaviors allowing a
hexapod robot to navigate in complex environments. We also show that modules
can be added and removed during operation without affecting the functionality
of the remaining controller. Finally, the control approach was successfully
demonstrated on a physical hexapod robot. Taken together, our study reveals a
significant step towards fast automatic design of versatile neural locomotion
control for complex robotic systems.
