LapGym

This repository introduces LapGym, an open-source framework for Reinforcement Learning (RL) in robot-assisted laparoscopic surgery. It contains the following submodules:

• sofa_env: Defines reinforcement learning environments for robot-assisted surgery
• sofa_godot: Provides a Godot plugin to visually create new SOFA scenes
• sofa_zoo: Provides the code for the reinforcement learning experiments described in LapGym paper

This work has been awarded with the Public’s prize at the SOFA Week 2023.

Authors present LapGym, a framework for building Reinforcement Learning (RL) environments for RALS that models the challenges posed by surgical tasks, and sofa_env, a diverse suite of 12 environments. Motivated by surgical training, these environments are organized into 4 tracks: Spatial Reasoning, Deformable Object Manipulation & Grasping, Dissection, and Thread Manipulation. Each environment is highly parametrizable for increasing difficulty, resulting in a high performance ceiling for new algorithms. Scheikl et al. aim to provide a challenging, standard environment suite for further development of RL for RALS, ultimately helping to realize the full potential of cognitive surgical robotics.

Related publications

This repository contains the code for the paper “LapGym-an open source framework for reinforcement learning in robot-assisted laparoscopic surgery – Paul Scheikl et al. – JMLR2023.

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