  Robot manipulation relying on learned object-centric descriptors became
popular in recent years. Visual descriptors can easily describe manipulation
task objectives, they can be learned efficiently using self-supervision, and
they can encode actuated and even non-rigid objects. However, learning robust,
view-invariant keypoints in a self-supervised approach requires a meticulous
data collection approach involving precise calibration and expert supervision.
In this paper we introduce Cycle-Correspondence Loss (CCL) for view-invariant
dense descriptor learning, which adopts the concept of cycle-consistency,
enabling a simple data collection pipeline and training on unpaired RGB camera
views. The key idea is to autonomously detect valid pixel correspondences by
attempting to use a prediction over a new image to predict the original pixel
in the original image, while scaling error terms based on the estimated
confidence. Our evaluation shows that we outperform other self-supervised
RGB-only methods, and approach performance of supervised methods, both with
respect to keypoint tracking as well as for a robot grasping downstream task.
