CRESSim: Simulator for Advancing Surgical Autonomy

Overview

Realistic and real-time surgical simulators play an increasingly important role in surgical robotics research, such as surgical robot learning and automation, and surgical skills assessment. The ability to simulate diverse objects and contact-rich manipulation tasks, such as tissue cutting and blood suction, is important because they commonly arise in surgery. CRESSim enables simulation of surgical tasks involving different instruments, soft tissue, and body fluids, and incorporates the da Vinci Research Kit (dVRK) console and master tool manipulators (MTMs) for virtual-reality teleoperation.

Our latest engine, CRESSim-Neo, is a batched GPU position-based dynamics (PBD) engine for rigid bodies, deformable tissue, fluids, and strands, with batched rendering, surgery-specific sensing, and a GPU-resident data pipeline for scalable surgical robot learning.

If you find this project helpful for your research, please consider citing one or more of the papers listed below.

New! Learn about CRESSim-Neo, our batched GPU simulation engine for surgical robotics and robot learning, in our latest paper.

Project Papers

[2026] CRESSim-Neo: A Batched GPU Simulation Engine for Surgical Robotics and Robot Learning

Yafei Ou, Ahnaf Naheen, Tleukhan Mussin, Hans Jarales, Melwin Chacko Moncy, Mahdi Tavakoli
arXiv preprint [arxiv] [bib] [code] [website]

[2026] Simulation of Surgical Suturing Using Position-Based Dynamics and the Material Point Method for Robot Reinforcement Learning

Tleukhan Mussin, Yafei Ou, Mahdi Tavakoli
IEEE RAS/EMBS 11th International Conference on Biomedical Robotics and Biomechatronics (BioRob) [arxiv] [bib]

[2025] CRESSim-MPM: A Material Point Method Library for Surgical Soft Body Simulation with Cutting and Suturing

Yafei Ou, Mahdi Tavakoli
2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) [paper] [arxiv] [bib] [code]

[2024] Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning

Yafei Ou, Mahdi Tavakoli
IEEE Transactions on Automation Science and Engineering [paper] [arxiv] [bib] [code]

[2024] From Decision to Action in Surgical Autonomy: Multi-Modal Large Language Models for Robot-Assisted Blood Suction

Sadra Zargarzadeh, Maryam Mirzaei, Yafei Ou, Mahdi Tavakoli
IEEE Robotics and Automation Letters [paper] [arxiv] [bib]

[2024] A Realistic Surgical Simulator for Non-Rigid and Contact-Rich Manipulation in Surgeries with the da Vinci Research Kit

Yafei Ou, Sadra Zargarzadeh, Paniz Sedighi, Mahdi Tavakoli
2024 21st International Conference on Ubiquitous Robots (UR) [paper] [arxiv] [bib]

[2024] Autonomous blood suction for robot-assisted surgery: A sim-to-real reinforcement learning approach

Yafei Ou, Abed Soleymani, Xingyu Li, Mahdi Tavakoli
IEEE Robotics and Automation Letters [paper] [bib]