# Autonomous Catheterization with Open-source Simulator and Expert Trajectory

arXiv: https://arxiv.org/abs/2401.09059 · HTML: https://mlsanity.com/paper/2401.09059/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 77 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 22 of 22)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization](https://mlsanity.com/paper/1603.00448/) (2016)
- [Socially Compliant Navigation through Raw Depth Inputs with Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1710.02543/) (2017)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention](https://mlsanity.com/paper/2006.09117/) (2020)
- [Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks](https://mlsanity.com/paper/1811.02759/) (2018)
- [Light-weight Deformable Registration using Adversarial Learning with Distilling Knowledge](https://mlsanity.com/paper/2110.01293/) (2021)
- [Deep reinforcement learning for guidewire navigation in coronary artery phantom](https://mlsanity.com/paper/2110.01840/) (2021)
- [3D Perception based Imitation Learning under Limited Demonstration for Laparoscope Control in Robotic Surgery](https://mlsanity.com/paper/2204.03195/) (2022)
- [Automatic catheter detection in pediatric X-ray images using a scale-recurrent network and synthetic data](https://mlsanity.com/paper/1806.00921/) (2018)
- [Towards Autonomous Eye Surgery by Combining Deep Imitation Learning with Optimal Control](https://mlsanity.com/paper/2011.07778/) (2020)
- [Detecting the Sensing Area of A Laparoscopic Probe in Minimally Invasive Cancer Surgery](https://mlsanity.com/paper/2307.03662/) (2023)
- [Shape-Sensitive Loss for Catheter and Guidewire Segmentation](https://mlsanity.com/paper/2311.11205/) (2023)
- [A Novel Sample-efficient Deep Reinforcement Learning with Episodic Policy Transfer for PID-Based Control in Cardiac Catheterization Robots](https://mlsanity.com/paper/2110.14941/) (2021)
- [Translating Simulation Images to X-ray Images via Multi-Scale Semantic Matching](https://mlsanity.com/paper/2304.07693/) (2023)
- [3D Guidewire Shape Reconstruction from Monoplane Fluoroscopic Images](https://mlsanity.com/paper/2311.11209/) (2023)

## Built on by (most cited first, 4 of 4)

- [CathAction: A Benchmark for Endovascular Intervention Understanding](https://mlsanity.com/paper/2408.13126/) (2024)
- [Robust Path Planning via Learning from Demonstrations for Robotic Catheters in Deformable Environments](https://mlsanity.com/paper/2402.00537/) (2024)
- [Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions](https://mlsanity.com/paper/2504.15327/) (2025)
- [SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation](https://mlsanity.com/paper/2501.04515/) (2025)

## Abstract (arXiv)

Endovascular robots have been actively developed in both academia and industry. However, progress toward autonomous catheterization is often hampered by the widespread use of closed-source simulators and physical phantoms. Additionally, the acquisition of large-scale datasets for training machine learning algorithms with endovascular robots is usually infeasible due to expensive medical procedures. In this chapter, we introduce CathSim, the first open-source simulator for endovascular intervention to address these limitations. CathSim emphasizes real-time performance to enable rapid development and testing of learning algorithms. We validate CathSim against the real robot and show that our simulator can successfully mimic the behavior of the real robot. Based on CathSim, we develop a multimodal expert navigation network and demonstrate its effectiveness in downstream endovascular navigation tasks. The intensive experimental results suggest that CathSim has the potential to significantly accelerate research in the autonomous catheterization field. Our project is publicly available at https://github.com/airvlab/cathsim.