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

**Authors:** Masoud Moghani, Lars Doorenbos, William Chung-Ho Panitch, Sean Huver, Mahdi Azizian, Ken Goldberg, Animesh Garg  
**arXiv:** [2405.05226](https://arxiv.org/abs/2405.05226)  
**Date:** 2024-05-08  
**Summary page:** https://www.summarizepaper.com/fr/paper/2405.05226v1/

## Abstract

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