  In human-robot collaboration, unintentional physical contacts occur in the
form of collisions and clamping, which must be detected and classified
separately for a reaction. If certain collision or clamping situations are
misclassified, reactions might occur that make the true contact case more
dangerous. This work analyzes data-driven modeling based on physically modeled
features like estimated external forces for clamping and collision
classification with a real parallel robot. The prediction reliability of a
feedforward neural network is investigated. Quantification of the
classification uncertainty enables the distinction between safe versus
unreliable classifications and optimal reactions like a retraction movement for
collisions, structure opening for the clamping joint, and a fallback reaction
in the form of a zero-g mode. This hypothesis is tested with experimental data
of clamping and collision cases by analyzing dangerous misclassifications and
then reducing them by the proposed uncertainty quantification. Finally, it is
investigated how the approach of this work influences correctly classified
clamping and collision scenarios.
