用语言指导的分层策略实现手术全程自主，成功率100%。
SRT-H: A Hierarchical Framework for Autonomous Surgery via Language Conditioned Imitation Learning
- 高阶用语言规划任务，低阶生成机器人轨迹，分层协同
- 在8个未见离体胆囊上实现100%成功率，全程无人干预
- 适合追求手术自动化与可解释性研究者参考
自主手术研究多集中于受控环境下的简单任务自动化，但真实手术需在长时间内进行灵巧操作，并适应人体组织的固有变异性。现有基于逻辑或传统端到端学习的方法难以应对这些挑战。为此，我们提出一种分层框架，用于执行灵巧且长时程的外科步骤。该方法采用高层策略进行任务规划，低层策略生成机器人轨迹。高层规划器在语言空间中生成任务级或修正指令，引导机器人完成长时程步骤并纠正低层策略的误差。我们在胆囊切除术这一常见微创手术的离体实验中验证了该框架，并通过消融实验评估系统关键组件。结果表明，该方法在8个未见过的离体胆囊上实现了100%成功率，全程完全自主运行，无需人工干预。本工作首次实现手术步骤级的自主性，标志着自主外科系统向临床应用迈出关键一步。
原文摘要 · Abstract (English)
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.
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