# ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity

arXiv: https://arxiv.org/abs/2404.16027 · HTML: https://mlsanity.com/paper/2404.16027/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2024.

## Built on by (most cited first, 16 of 16)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning](https://mlsanity.com/paper/2505.10251/) (2025)
- [GP-VLS: A general-purpose vision language model for surgery](https://mlsanity.com/paper/2407.19305/) (2024)
- [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://mlsanity.com/paper/2604.27106/) (2026)
- [PokeFlex: A Real-World Dataset of Volumetric Deformable Objects for Robotics](https://mlsanity.com/paper/2410.07688/) (2024)
- [NLiPsCalib: An Efficient Calibration Framework for High-Fidelity 3D Reconstruction of Curved Visuotactile Sensors](https://mlsanity.com/paper/2603.09319/) (2026)
- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [Learning Autonomous Surgical Irrigation and Suction with the da Vinci Research Kit Using Reinforcement Learning](https://mlsanity.com/paper/2411.14622/) (2024)
- [dARt Vinci: Egocentric Data Collection for Surgical Robot Learning at Scale](https://mlsanity.com/paper/2503.05646/) (2025)
- [Safe Uncertainty-Aware Learning of Robotic Suturing](https://mlsanity.com/paper/2505.16596/) (2025)
- [MoDeSuite: Robot Learning Task Suite for Benchmarking Mobile Manipulation with Deformable Objects](https://mlsanity.com/paper/2507.21796/) (2025)
- [CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models](https://mlsanity.com/paper/2509.08015/) (2025)
- [Embedded Image-to-Image Translation for Efficient Sim-to-Real Transfer in Learning-based Robot-Assisted Soft Manipulation](https://mlsanity.com/paper/2409.10204/) (2024)
- [MarUco: A Markerless 6D Pose Estimation Framework for Closed-Loop Control of Surgical Continuum Manipulators](https://mlsanity.com/paper/2602.16365/) (2026)
- [Imitation Learning for Robot Assistance in Open Surgery: A Multi-Policy Evaluation on Suture Following](https://mlsanity.com/paper/2605.28736/) (2026)
- [SurgVLA-Bench: Towards Evaluating Vision-Language-Action Models for Laparoscopic Surgical Robotics](https://mlsanity.com/paper/2606.29247/) (2026)

## Abstract (arXiv)

Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present ORBIT-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering in NVIDIA Omniverse. We provide 14 benchmark surgical tasks for the da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR) which represent common subtasks in surgical training. ORBIT-Surgical leverages GPU parallelization to train reinforcement learning and imitation learning algorithms to facilitate study of robot learning to augment human surgical skills. ORBIT-Surgical also facilitates realistic synthetic data generation for active perception tasks. We demonstrate ORBIT-Surgical sim-to-real transfer of learned policies onto a physical dVRK robot. Project website: orbit-surgical.github.io