oai:arXiv.org:2405.05226
Computer Science
2024
5/15/2024
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
Moghani, Masoud,Doorenbos, Lars,Panitch, William Chung-Ho,Huver, Sean,Azizian, Mahdi,Goldberg, Ken,Garg, Animesh, 2024, SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants