Autonomous Catheter Navigation

In progress: RL for endovascular robotics (ARPA-H & DARPA project)

Jul 2026 to present

This project is in active development. It started in July 2026; this page will grow as results mature.

Endovascular procedures mean steering a catheter through branching vasculature under imaging, and the skill ceiling is high enough that outcomes depend heavily on operator experience. The long-term goal is autonomy that makes the procedure repeatable.

A learned policy steering a catheter through vasculature in NVIDIA's Isaac for Healthcare endoluminal simulator.

What I’m building

The architecture splits navigation across two levels: a high-level policy picks waypoints through the vascular tree, and a low-level control policy drives the catheter tip to each one. I build the low-level policy; a teammate works on the high level. The low-level problem is where contact physics, tortuous geometry, and the catheter’s own compliance make naive control fail.

I train these policies with PPO and domain randomization in GPU-accelerated physics simulation of patient-derived vasculature, working in NVIDIA’s Isaac for Healthcare (i4h) endoluminal simulator. I also made sampling-based MPC tractable in simulation by building state snapshot/restore for the GPU catheter-physics solver, and contributed three catheter simulation demos to Newton, the open-source physics engine from NVIDIA, Google DeepMind, and Disney Research.