SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning
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arXiv
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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
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| _version_ | 1866918085772967936 |
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| author | Kim, Ji Woong Chen, Juo-Tung Hansen, Pascal Shi, Lucy X. Goldenberg, Antony Schmidgall, Samuel Scheikl, Paul Maria Deguet, Anton White, Brandon M. Tsai, De Ru Cha, Richard Jopling, Jeffrey Finn, Chelsea Krieger, Axel |
| author_facet | Kim, Ji Woong Chen, Juo-Tung Hansen, Pascal Shi, Lucy X. Goldenberg, Antony Schmidgall, Samuel Scheikl, Paul Maria Deguet, Anton White, Brandon M. Tsai, De Ru Cha, Richard Jopling, Jeffrey Finn, Chelsea Krieger, Axel |
| contents | Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning approaches. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach utilizes a high-level policy for task planning and a low-level policy for generating robot trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and correct for the low-level policy's errors. We validate our framework through ex vivo experiments on cholecystectomy, a commonly-practiced minimally invasive procedure, and conduct ablation studies to evaluate key components of the system. Our method achieves a 100\% success rate across eight unseen ex vivo gallbladders, operating fully autonomously without human intervention. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10251 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning Kim, Ji Woong Chen, Juo-Tung Hansen, Pascal Shi, Lucy X. Goldenberg, Antony Schmidgall, Samuel Scheikl, Paul Maria Deguet, Anton White, Brandon M. Tsai, De Ru Cha, Richard Jopling, Jeffrey Finn, Chelsea Krieger, Axel Robotics Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning approaches. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach utilizes a high-level policy for task planning and a low-level policy for generating robot trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and correct for the low-level policy's errors. We validate our framework through ex vivo experiments on cholecystectomy, a commonly-practiced minimally invasive procedure, and conduct ablation studies to evaluate key components of the system. Our method achieves a 100\% success rate across eight unseen ex vivo gallbladders, operating fully autonomously without human intervention. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems. |
| title | SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2505.10251 |