  Datasets are an essential component for training effective machine learning
models. In particular, surgical robotic datasets have been key to many advances
in semi-autonomous surgeries, skill assessment, and training. Simulated
surgical environments can enhance the data collection process by making it
faster, simpler and cheaper than real systems. In addition, combining data from
multiple robotic domains can provide rich and diverse training data for
transfer learning algorithms. In this paper, we present the DESK (Dexterous
Surgical Skill) dataset. It comprises a set of surgical robotic skills
collected during a surgical training task using three robotic platforms: the
Taurus II robot, Taurus II simulated robot, and the YuMi robot. This dataset
was used to test the idea of transferring knowledge across different domains
(e.g. from Taurus to YuMi robot) for a surgical gesture classification task
with seven gestures. We explored three different scenarios: 1) No transfer, 2)
Transfer from simulated Taurus to real Taurus and 3) Transfer from Simulated
Taurus to the YuMi robot. We conducted extensive experiments with three
supervised learning models and provided baselines in each of these scenarios.
Results show that using simulation data during training enhances the
performance on the real robot where limited real data is available. In
particular, we obtained an accuracy of 55% on the real Taurus data using a
model that is trained only on the simulator data. Furthermore, we achieved an
accuracy improvement of 34% when 3% of the real data is added into the training
process.
