  Direct physical interaction with robots is becoming increasingly important in
flexible production scenarios, but robots without protective fences also pose a
greater risk to the operator. In order to keep the risk potential low,
relatively simple measures are prescribed for operation, such as stopping the
robot if there is physical contact or if a safety distance is violated.
Although human injuries can be largely avoided in this way, all such solutions
have in common that real cooperation between humans and robots is hardly
possible and therefore the advantages of working with such systems cannot
develop its full potential. In human-robot collaboration scenarios, more
sophisticated solutions are required that make it possible to adapt the robot's
behavior to the operator and/or the current situation. Most importantly, during
free robot movement, physical contact must be allowed for meaningful
interaction and not recognized as a collision. However, here lies a key
challenge for future systems: detecting human contact by using robot
proprioception and machine learning algorithms. This work uses the Deep Metric
Learning (DML) approach to distinguish between non-contact robot movement,
intentional contact aimed at physical human-robot interaction, and collision
situations. The achieved results are promising and show show that DML achieves
98.6\% accuracy, which is 4\% higher than the existing standards (i.e. a deep
learning network trained without DML). It also indicates a promising
generalization capability for easy portability to other robots (target robots)
by detecting contact (distinguishing between contactless and intentional or
accidental contact) without having to retrain the model with target robot data.
