Introducing autonomy into robotic minimally-invasive surgery has the capability to improve surgical performance. While pure autonomy is not yet feasible for many surgical procedures, human-robot collaboration models, which operate on a combination of human and autonomous input, have the potential to foster the transition to autonomy and offer enhanced performance over pure human control. Building on our previous work on human-robot collaboration models, we focus on designing and testing collaborative control models based on human intent for an inclusion segmentation task. We seek to use learning techniques and support vector machine classifiers to predict both local regions of human interest and the level of interest in these regions; these predictions will then inform the role of the robotic agent in the task. The intent-based collaboration models will be tested in a human subject study for the inclusion segmentation task against teleoperation and pure autonomy to assess task performance. We anticipate that these collaboration models will outperform both pure human and pure autonomous control.

Increasing the level of autonomy in robot-assisted surgery has the potential to improve the safety, speed, and applicability of robot-assisted surgical systems. To facilitate the development and incorporation of robot autonomy in clinical settings, human-robot collaboration models have been suggested in which human and robotic agents work together to accomplish a task. In this work, we measure performance of several human-robot collaboration models in two experiments based on the task of segmenting a stiff inclusion in soft tissue, which simulates a tumor. In the inclusion segmentation experiment, twelve participants explored an artificial tissue and identified the inclusion boundary under the collaboration models of (1) teleoperation, (2) supervised control, (3) traded control, and (4) full autonomy. In the boundary identification experiment, we isolate the performance of human and robotic agents in the boundary identification sub-task; participants and a robotic agent independently identified the boundary of four virtually palpated tissues. Results from the inclusion segmentation experiment indicate that human agents complete the task faster; teleoperation had the fastest task times. Results of both experiments indicate that the robotic agent identifies boundaries with higher sensitivity and less variance than human agents. This indicates that task accuracy increases when a robotic agent segments the boundary, while including a human agent can decrease the overall task time.