CathSim

Installation Procedure

1. Download MuJoCo

mkdir .mujoco
cd .mujoco
wget https://github.com/deepmind/mujoco/releases/download/2.1.0/mujoco210-linux-x86_64.tar.gz
tar xvzf mujoco210-linux-x86_64.tar.gz
rm -rf mujoco210-linux-x86_64.tar.gz

3. Install Dependencies

sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3

3. Add the following to the .bashrc file:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/user/.mujoco/mujoco210/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/nvidia
export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libGLEW.so

4. Install the environment

git clone [email protected]:tudorjnu/cathsim.git
cd cathsim
pip install -e .

Quick start

import cathsim_env
import gym


env = gym.make('cathsim_env/CathSim-v0', 
                scene=1,  # 1 or 2 for Type-I Aortic Arch and Type-II Aortic Arch 
                target="bca",  # "bca" or "lcca"
                obs_type="internal", # image or internal
                image_size=128, 
                delta=0.008, # the distance threshold between catheter head and target
                success_reward=10.0, # the reward for reaching the target
                compute_force=False, # whether to compute the force
                dense_reward=True, # whether to use a dense reward or a sparse reward,
                )

obs = env.reset()
for _ in range(2000):
    action = env.action_space.sample()
    obs, reward, done, info = env.step(action)
    env.render()

Citation

@article{jianu2022cathsim,
  title={CathSim: An Open-source Simulator for Autonomous Cannulation},
  author={Jianu, Tudor and Huang, Baoru and Abdelaziz, Mohamed EMK and Vu, Minh Nhat and Fichera, Sebastiano and Lee, Chun-Yi and Berthet-Rayne, Pierre and Nguyen, Anh and others},
  journal={arXiv preprint arXiv:2208.01455},
  year={2022}
}