# SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants

arXiv: https://arxiv.org/abs/2405.05226 · HTML: https://mlsanity.com/paper/2405.05226/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2024.

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

- [GP-VLS: A general-purpose vision language model for surgery](https://mlsanity.com/paper/2407.19305/) (2024)
- [Speak, Segment, Track, Navigate: An Interactive System for Video-Guided Skull-Base Surgery](https://mlsanity.com/paper/2603.16024/) (2026)
- [OR-VSKC: Resolving Visual-Semantic Knowledge Conflicts in Operating Rooms with Synthetic Data-Guided Alignment](https://mlsanity.com/paper/2506.22500/) (2025)
- [SurgiCLAW: An Interpretable Atomic-Skill Agent for Precision Robotic Micromanipulation](https://mlsanity.com/paper/2610.15766/) (2026)

## Abstract (arXiv)

In this work, we present SuFIA, the first framework for natural language-guided augmented dexterity for robotic surgical assistants. SuFIA incorporates the strong reasoning capabilities of large language models (LLMs) with perception modules to implement high-level planning and low-level control of a robot for surgical sub-task execution. This enables a learning-free approach to surgical augmented dexterity without any in-context examples or motion primitives. SuFIA uses a human-in-the-loop paradigm by restoring control to the surgeon in the case of insufficient information, mitigating unexpected errors for mission-critical tasks. We evaluate SuFIA on four surgical sub-tasks in a simulation environment and two sub-tasks on a physical surgical robotic platform in the lab, demonstrating its ability to perform common surgical sub-tasks through supervised autonomous operation under challenging physical and workspace conditions. Project website: orbit-surgical.github.io/sufia