  Surgical robot simulation platform plays a crucial role in enhancing training
efficiency and advancing research on robot learning. Much effort have been made
by scholars on developing open-sourced surgical robot simulators to facilitate
research. We also developed SurRoL formerly, an open-source, da Vinci Research
Kit (dVRK) compatible and interactive embodied environment for robot learning.
Despite its advancements, the simulation of soft bodies still remained a major
challenge within the open-source platforms available for surgical robotics. To
this end, we develop an interactive physically based soft body simulation
framework and integrate it to SurRoL. Specifically, we utilized a
high-performance adaptation of the Material Point Method (MPM) along with the
Neo-Hookean model to represent the deformable tissue. Lagrangian particles are
used to track the motion and deformation of the soft body throughout the
simulation and Eulerian grids are leveraged to discretize space and facilitate
the calculation of forces, velocities, and other physical quantities. We also
employed an efficient collision detection and handling strategy to simulate the
interaction between soft body and rigid tool of the surgical robot. By
employing the Taichi programming language, our implementation harnesses
parallel computing to boost simulation speed. Experimental results show that
our platform is able to simulate soft bodies efficiently with strong physical
interpretability and plausible visual effects. These new features in SurRoL
enable the efficient simulation of surgical tasks involving soft tissue
manipulation and pave the path for further investigation of surgical robot
learning. The code will be released in a new branch of SurRoL github repo.
