Unsupervised learning of camera exposure control using randomly connected neural networks

We use webcams on single board computers for vision-based control of flying robots. In that context we consider autonomous acquisition (bootstrapping) of exposure and gain control policies for the digital cameras. The policies are generated by neural networks with random connectivity which can be regarded as nonlinear expansion kernels acting on the input. We consider both feed-forward and recursive networks and apply these structures to learning the required policies. The camera represents an embodied robotic subsystem which is subject to temporal delays in its response. The performance measure is based on selective regions of interest in the image. The contribution of this paper is a complete embodied autonomous learning loop.