  Indirect simultaneous positioning (ISP), where internal tissue points are
placed at desired locations indirectly through the manipulation of boundary
points, is a type of subtask frequently performed in robotic surgeries.
Although challenging due to complex tissue dynamics, automating the task can
potentially reduce the workload of surgeons. This paper presents a sim-to-real
framework for learning to automate the task without interacting with a real
environment, and for planning preoperatively to find the grasping points that
minimize local tissue deformation. A control policy is learned using deep
reinforcement learning (DRL) in the FEM-based simulation environment and
transferred to real-world situation. Grasping points are planned in the
simulator by utilizing the trained policy using Bayesian optimization (BO).
Inconsistent simulation performance is overcome by formulating the problem as a
state augmented Markov decision process (MDP). Experimental results show that
the learned policy places the internal tissue points accurately, and that the
planned grasping points yield small tissue deformation among the trials. The
proposed learning and planning scheme is able to automate internal tissue point
manipulation in surgeries and has the potential to be generalized to complex
surgical scenarios.
