  Human-robot shared control, which integrates the advantages of both humans
and robots, is an effective approach to facilitate efficient surgical
operation. Learning from demonstration (LfD) techniques can be used to automate
some of the surgical subtasks for the construction of the shared control
framework. However, a sufficient amount of data is required for the robot to
learn the manoeuvres. Using a surgical simulator to collect data is a less
resource-demanding approach. With sim-to-real adaptation, the manoeuvres
learned from a simulator can be transferred to a physical robot. To this end,
we propose a sim-to-real adaptation method to construct a human-robot shared
control framework for robotic surgery.
  In this paper, a desired trajectory is generated from a simulator using LfD
method, while dynamic motion primitives (DMPs) based method is used to transfer
the desired trajectory from the simulator to the physical robotic platform.
Moreover, a role adaptation mechanism is developed such that the robot can
adjust its role according to the surgical operation contexts predicted by a
neural network model. The effectiveness of the proposed framework is validated
on the da Vinci Research Kit (dVRK). Results of the user studies indicated that
with the adaptive human-robot shared control framework, the path length of the
remote controller, the total clutching number and the task completion time can
be reduced significantly. The proposed method outperformed the traditional
manual control via teleoperation.
