Training in divergent and convergent force fields during 6-DOF teleoperation with a robot-assisted surgical system

The technical skills of surgeons directly affect patient outcomes, yet how to train surgeons in a way that maximizes their learning speed and optimizes their performance is an open question. Recent studies in human motor learning have shown benefits of using force fields during training in point-to-point reaching tasks. Teleoperation systems enable the application of these force fields during the learning of more complex and real-world activities. We performed a study in which participants used the da Vinci Research Kit, a teleoperated robot-assisted surgical system, to perform a peg transfer task — a standard manipulation task used in minimally invasive surgery training. We investigated the effect on learning of training in three different groups: (1) without applying any force, (2) with a divergent force field, which pushes the user away from the desired path if they deviate from it, and (3) with a convergent force field, which pushes the user back to the desired path. We found no statistically significant differences in performance among the different training groups at the end of the experiment, but some differences were evident throughout the training. Thus, training in the divergent and convergent fields may involve different learning mechanisms, but does not worsen performance.