  This paper considers the final approach phase of visual-closed-loop grasping
where the RGB-D camera is no longer able to provide valid depth information.
Many current robotic grasping controllers are not closed-loop and therefore
fail for moving objects. Closed-loop grasp controllers based on RGB-D imagery
can track a moving object, but fail when the sensor's minimum object distance
is violated just before grasping. To overcome this we propose the use of
image-based visual servoing (IBVS) to guide the robot to the object-relative
grasp pose using camera RGB information. IBVS robustly moves the camera to a
goal pose defined implicitly in terms of an image-plane feature configuration.
In this work, the goal image feature coordinates are predicted from RGB-D data
to enable RGB-only tracking once depth data becomes unavailable -- this enables
more reliable grasping of previously unseen moving objects. Experimental
results are provided.
