Skip to content
This repository has been archived by the owner on Aug 22, 2024. It is now read-only.

Latest commit

 

History

History
45 lines (32 loc) · 1.42 KB

README.md

File metadata and controls

45 lines (32 loc) · 1.42 KB

⚠️ Deprecation Notice

This repository is deprecated and is no longer actively maintained.

The repository will be publicly archived, meaning it will remain accessible in a read-only state. Please consider using SofaGym and LapGym as they have much better implementations and are actively maintained.


Soft Tissue Manipulation

This repo contains the main code for the paper Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation Using Reinforcement Learning.

Requirements

Usage

Please refer to test_env.py.

env = SkinTissueEnvContinuousRandom(obs_sequence_length=1, params=params, render_mode="pyplot", randomize=True)
env.reset()

for _ in range(20):
    act = env.action_space.sample()
    env.step(act)

Citation

@article{ou2023sim,
  title={Sim-to-Real Surgical Robot Learning and Autonomous Planning for Internal Tissue Points Manipulation Using Reinforcement Learning},
  author={Ou, Yafei and Tavakoli, Mahdi},
  journal={IEEE Robotics and Automation Letters},
  volume={8},
  number={5},
  pages={2502--2509},
  year={2023},
  publisher={IEEE}
}