Neural sensorimotor primitives for vision-controlled flying robots

Vision-based control of aerial robots adds the challenge of visual sensing on top of the basic control problems. Here we pursue the approach of using sensor-coupled motion primitives in order to tie together both the sensor and motor components of flying robots to leverage sensorimotor interaction in a fundamental manner. We also consider the temporal scale over which autonomy is to be achieved. Longer term autonomy requires significant adaptive capacity of the respective control structures. We investigate linear combinations of some nonlinear network activity for the generation of motor output and ultimately, behaviour. In particular we are interested in how such networks can be set up to autonomously learn a desired behaviour. For that, we are looking at bootstrapping of motor primitives. The contribution of this work is a neurally inspired architecture for the representation of sensorimotor primitives capable of online learning on a flying robot.