  Surgeons normally need surgical scissors and tissue grippers to cut through a
deformable surgical tissue. The cutting accuracy depends on the skills to
manipulate these two tools. Such skills are part of basic surgical skills
training as in the Fundamentals of Laparoscopic Surgery. The gripper is used to
pinch a point on the surgical sheet and pull the tissue to a certain direction
to maintain the tension while the scissors cut through a trajectory. As the
surgical materials are deformable, it requires a comprehensive tensioning
policy to yield appropriate tensioning direction at each step of the cutting
process. Automating a tensioning policy for a given cutting trajectory will
support not only the human surgeons but also the surgical robots to improve the
cutting accuracy and reliability. This paper presents a multiple pinch point
approach to modelling an autonomous tensioning planner based on a deep
reinforcement learning algorithm. Experiments on a simulator show that the
proposed method is superior to existing methods in terms of both performance
and robustness.
