SuFIA: 言語ガイドによる手術支援ロボットの拡張巧緻性
SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
大規模言語モデルと知覚モジュールを組み合わせ、自然言語指示で手術支援ロボットのサブタスクを学習なしに実行する枠組みを提案し、シミュレーションと実機で検証した。
著者: Masoud Moghani, Lars Doorenbos, William Chung-Ho Panitch, Sean Huver, Mahdi Azizian, Ken Goldberg, Animesh Garg
分類: cs.RO
原文アブストラクト
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
