  A significant problem of using deep learning techniques is the limited amount
of data available for training. There are some datasets available for the
popular problems like item recognition and classification or self-driving cars,
however, it is very limited for the industrial robotics field. In previous
work, we have trained a multi-objective Convolutional Neural Network (CNN) to
identify the robot body in the image and estimate 3D positions of the joints by
using just a 2D image, but it was limited to a range of robots produced by
Universal Robots (UR). In this work, we extend our method to work with a new
robot arm - Kuka LBR iiwa, which has a significantly different appearance and
an additional joint. However, instead of collecting large datasets once again,
we collect a number of smaller datasets containing a few hundred frames each
and use transfer learning techniques on the CNN trained on UR robots to adapt
it to a new robot having different shapes and visual features. We have proven
that transfer learning is not only applicable in this field, but it requires
smaller well-prepared training datasets, trains significantly faster and
reaches similar accuracy compared to the original method, even improving it on
some aspects.
