  Short response time is critical for future military medical operations in
austere settings or remote areas. Such effective patient care at the point of
injury can greatly benefit from the integration of semi-autonomous robotic
systems. To achieve autonomy, robots would require massive libraries of
maneuvers. While this is possible in controlled settings, obtaining surgical
data in austere settings can be difficult. Hence, in this paper, we present the
Dexterous Surgical Skill (DESK) database for knowledge transfer between robots.
The peg transfer task was selected as it is one of 6 main tasks of laparoscopic
training. Also, we provide a ML framework to evaluate novel transfer learning
methodologies on this database. The collected DESK dataset comprises a set of
surgical robotic skills using the four robotic platforms: Taurus II, simulated
Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different
learning scenarios: no-transfer and domain-transfer. In the no-transfer
scenario, the training and testing data were obtained from the same domain;
whereas in the domain-transfer scenario, the training data is a blend of
simulated and real robot data that is tested on a real robot. Using simulation
data enhances the performance of the real robot where limited or no real data
is available. The transfer model showed an accuracy of 81% for the YuMi robot
when the ratio of real-to-simulated data was 22%-78%. For Taurus II and da
Vinci robots, the model showed an accuracy of 97.5% and 93% respectively,
training only with simulation data. Results indicate that simulation can be
used to augment training data to enhance the performance of models in real
scenarios. This shows the potential for future use of surgical data from the
operating room in deployable surgical robots in remote areas.
