  Visual servoing enables robotic systems to perform accurate closed-loop
control, which is required in many applications. However, existing methods
either require precise calibration of the robot kinematic model and cameras or
use neural architectures that require large amounts of data to train. In this
work, we present a method for unsupervised learning of visual servoing that
does not require any prior calibration and is extremely data-efficient. Our key
insight is that visual servoing does not depend on identifying the veridical
kinematic and camera parameters, but instead only on an accurate generative
model of image feature observations from the joint positions of the robot. We
demonstrate that with our model architecture and learning algorithm, we can
consistently learn accurate models from less than 50 training samples (which
amounts to less than 1 min of unsupervised data collection), and that such
data-efficient learning is not possible with standard neural architectures.
Further, we show that by using the generative model in the loop and learning
online, we can enable a robotic system to recover from calibration errors and
to detect and quickly adapt to possibly unexpected changes in the robot-camera
system (e.g. bumped camera, new objects).
