Purpose - Skullbase surgery demands exceptional precision when removing bone in the lateral skull base. Robotic assistance can alleviate the effect of human sensory-motor limitations. However, the stiffness and inertia of the robot can significantly impact the surgeon's perception and control of the tool-to-tissue interaction forces. Methods - We present a situational-aware, force control technique aimed at regulating interaction forces during robot-assisted skullbase drilling. The contextual interaction information derived from the digital twin environment is used to enhance sensory perception and suppress undesired high forces. Results - To validate our approach, we conducted initial feasibility experiments involving a medical and two engineering students. The experiment focused on further drilling around critical structures following cortical mastoidectomy. The experiment results demonstrate that robotic assistance coupled with our proposed control scheme effectively limited undesired interaction forces when compared to robotic assistance without the proposed force control. Conclusions - The proposed force control techniques show promise in significantly reducing undesired interaction forces during robot-assisted skullbase surgery. These findings contribute to the ongoing efforts to enhance surgical precision and safety in complex procedures involving the lateral skull base.

翻译：目的 - 颅底手术在去除侧颅底骨质时需要极高的精度。机器人辅助可以减轻人类感觉运动限制的影响。然而，机器人的刚度和惯性会显著影响外科医生对工具与组织间交互力的感知与控制。方法 - 我们提出一种情境感知力控制技术，旨在调节机器人辅助颅底钻孔过程中的交互力。利用数字孪生环境导出的上下文交互信息来增强感官感知并抑制非期望的高力。结果 - 为验证该方法，我们开展了一项初步可行性实验，涉及一名医学生和两名工程专业学生。实验聚焦于在皮质乳突切除术后围绕关键结构进行进一步钻孔。结果表明，与未采用本力控制的机器人辅助相比，结合我们提出的控制方案的机器人辅助有效限制了非期望的交互力。结论 - 所提出的力控制技术在显著减少机器人辅助颅底手术中的非期望交互力方面展现出潜力。这些发现为提升涉及侧颅底的复杂手术中的手术精度与安全性提供了持续助力。