# SuFIA-BC: Generating High Quality Demonstration Data for Visuomotor Policy Learning in Surgical Subtasks

arXiv: https://arxiv.org/abs/2504.14857 · HTML: https://mlsanity.com/paper/2504.14857/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action](https://mlsanity.com/paper/2606.15647/) (2026)
- [Confidence-aware Monocular Depth Estimation for Minimally Invasive Surgery](https://mlsanity.com/paper/2603.03571/) (2026)
- [MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception](https://mlsanity.com/paper/2609.02717/) (2026)

## Abstract (arXiv)

Behavior cloning facilitates the learning of dexterous manipulation skills, yet the complexity of surgical environments, the difficulty and expense of obtaining patient data, and robot calibration errors present unique challenges for surgical robot learning. We provide an enhanced surgical digital twin with photorealistic human anatomical organs, integrated into a comprehensive simulator designed to generate high-quality synthetic data to solve fundamental tasks in surgical autonomy. We present SuFIA-BC: visual Behavior Cloning policies for Surgical First Interactive Autonomy Assistants. We investigate visual observation spaces including multi-view cameras and 3D visual representations extracted from a single endoscopic camera view. Through systematic evaluation, we find that the diverse set of photorealistic surgical tasks introduced in this work enables a comprehensive evaluation of prospective behavior cloning models for the unique challenges posed by surgical environments. We observe that current state-of-the-art behavior cloning techniques struggle to solve the contact-rich and complex tasks evaluated in this work, regardless of their underlying perception or control architectures. These findings highlight the importance of customizing perception pipelines and control architectures, as well as curating larger-scale synthetic datasets that meet the specific demands of surgical tasks. Project website: https://orbit-surgical.github.io/sufia-bc/