In robot-assisted surgery, exploration and manipulation tasks can be achieved through collaboration among robotic and human agents. Collaboration models can potentially include multiple agents working towards a shared objective—a scenario referred to as multilateral manipulation. We present a flexible software framework to expedite development of various multilateral manipulation strategies. We demonstrate the effectiveness of an implementation of the framework in a palpation task. Five different collaboration models were tested in which the goal of the multilateral manipulation system is to segment a stiff inclusion from its surrounding soft tissue: three of these collaboration models used machine learning methods for segmentation, and two required human operator segmentation. The collaboration models tested were: 1) fully autonomous exploration of the tissue; 2) shared control between a human and robotic agent; 3) supervised control where the operator dictates commands to the robot; 4) traded control between the two agents; and 5) bilateral teleoperation. Results indicate tradeoffs in sensitivity, maximum force applied, safety implications, and duration of experiment among the five models.
['Kirk A. Nichols', 'Allison M. Okamura']
A Framework for Multilateral Manipulation in Surgical Tasks