  The field of collaborative robotics and human-robot interaction often focuses
on the prediction of human behaviour, while assuming the information about the
robot setup and configuration being known. This is often the case with fixed
setups, which have all the sensors fixed and calibrated in relation to the rest
of the system. However, it becomes a limiting factor when the system needs to
be reconfigured or moved. We present a deep learning approach, which aims to
solve this issue. Our method learns to identify and precisely localise the
robot in 2D camera images, so having a fixed setup is no longer a requirement
and a camera can be moved. In addition, our approach identifies the robot type
and estimates the 3D position of the robot base in the camera image as well as
3D positions of each of the robot joints. Learning is done by using a
multi-objective convolutional neural network with four previously mentioned
objectives simultaneously using a combined loss function. The multi-objective
approach makes the system more flexible and efficient by reusing some of the
same features and diversifying for each objective in lower layers. A fully
trained system shows promising results in providing an accurate mask of where
the robot is located and an estimate of its base and joint positions in 3D. We
compare the results to our previous approach of using cascaded convolutional
neural networks.
