SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916239353315328 |
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| author | Moghani, Masoud Doorenbos, Lars Panitch, William Chung-Ho Huver, Sean Azizian, Mahdi Goldberg, Ken Garg, Animesh |
| author_facet | Moghani, Masoud Doorenbos, Lars Panitch, William Chung-Ho Huver, Sean Azizian, Mahdi Goldberg, Ken Garg, Animesh |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05226 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants Moghani, Masoud Doorenbos, Lars Panitch, William Chung-Ho Huver, Sean Azizian, Mahdi Goldberg, Ken Garg, Animesh Robotics 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 |
| title | SuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants |
| topic | Robotics |
| url | https://arxiv.org/abs/2405.05226 |