Autonomous Soft Tissue Retraction Using Demonstration-Guided
  Reinforcement Learning
Amritpal Singh Wenqi Shi May D Wang
In the context of surgery, robots can provide substantial assistance by
performing small, repetitive tasks such as suturing, needle exchange, and
tissue retraction, thereby enabling surgeons to concentrate on more complex
aspects of the procedure. However, existing surgical task learning mainly
pertains to rigid body interactions, whereas the advancement towards more
sophisticated surgical robots necessitates the manipulation of soft bodies.
Previous work focused on tissue phantoms for soft tissue task learning, which
can be expensive and can be an entry barrier to research. Simulation
environments present a safe and efficient way to learn surgical tasks before
their application to actual tissue. In this study, we create a Robot Operating
System (ROS)-compatible physics simulation environment with support for both
rigid and soft body interactions within surgical tasks. Furthermore, we
investigate the soft tissue interactions facilitated by the patient-side
manipulator of the DaVinci surgical robot. Leveraging the pybullet physics
engine, we simulate kinematics and establish anchor points to guide the robotic
arm when manipulating soft tissue. Using demonstration-guided reinforcement
learning (RL) algorithms, we investigate their performance in comparison to
traditional reinforcement learning algorithms. Our in silico trials demonstrate
a proof-of-concept for autonomous surgical soft tissue retraction. The results
corroborate the feasibility of learning soft body manipulation through the
application of reinforcement learning agents. This work lays the foundation for
future research into the development and refinement of surgical robots capable
of managing both rigid and soft tissue interactions. Code is available at
https://github.com/amritpal-001/tissue_retract.