SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants

TL;DR

SuFIA introduces a natural language-guided framework that combines large language models with perception modules to enable autonomous, high-level planning and low-level control for robotic surgical assistants, enhancing dexterity without prior motion primitives.

Contribution

It is the first framework to integrate language-guided reasoning with perception for surgical robots, allowing learning-free, autonomous task execution with human-in-the-loop control.

Findings

Successfully performed surgical sub-tasks in simulation and physical environments.

Demonstrated robustness under challenging physical and workspace conditions.

Enabled autonomous operation without in-context examples or motion primitives.

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