  Robot-assisted surgery is an established clinical practice. The automatic
identification of surgical actions is needed for a range of applications,
including performance assessment of trainees and surgical process modeling for
autonomous execution and monitoring. However, supervised action identification
is not feasible, due to the burden of manually annotating recordings of
potentially complex and long surgical executions. Moreover, often few example
executions of a surgical procedure can be recorded. This paper proposes a novel
fast algorithm for unsupervised identification of surgical actions in a
standard surgical training task, the ring transfer, executed with da Vinci
Research Kit. Exploiting kinematic and semantic visual features automatically
extracted from a very limited dataset of executions, we are able to
significantly outperform state-of-the-art results on a dataset of non-expert
executions (58\% vs. 24\% F1-score), and improve performance in the presence of
noise, short actions and non-homogeneous workflows, i.e. non repetitive action
sequences.
