  Catching objects in-flight is an outstanding challenge in robotics. In this
paper, we present a closed-loop control system fusing data from two sensor
modalities: an RGB-D camera and a radar. To develop and test our method, we
start with an easy to identify object: a stuffed Piglet. We implement and
compare two approaches to detect and track the object, and to predict the
interception point. A baseline model uses colour filtering for locating the
thrown object in the environment, while the interception point is predicted
using a least squares regression over the physical ballistic trajectory
equations. A deep learning based method uses artificial neural networks for
both object detection and interception point prediction. We show that we are
able to successfully catch Piglet in 80% of the cases with our deep learning
approach.
