Robotic cochlear-implant (CI) insertion requires precise prediction and regulation of contact forces to minimize intracochlear trauma and prevent failure modes such as locking and buckling. Aligned with the integration of advanced medical imaging and robotics for autonomous, precision interventions, this paper presents a unified CT-to-simulation pipeline for contact-aware insertion planning and validation. We develop a low-dimensional, differentiable Cosserat-rod model of the electrode array coupled with frictional contact and pseudo-dynamics regularization to ensure continuous stick-slip transitions. Patient-specific cochlear anatomy is reconstructed from CT imaging and encoded via an analytic parametrization of the scala-tympani lumen, enabling efficient and differentiable contact queries through closest-point projection. Based on a differentiated equilibrium-constraint formulation, we derive an online direction-update law under an RCM-like constraint that suppresses lateral insertion forces while maintaining axial advancement. Simulations and benchtop experiments validate deformation and force trends, demonstrating reduced locking/buckling risk and improved insertion depth. The study highlights how CT-based imaging enhances modeling, planning, and safety capabilities in robot-assisted inner-ear procedures.

翻译：机器人辅助人工耳蜗（CI）电极植入需要精确预测和控制接触力，以最大程度减少耳蜗内创伤并防止锁定和屈曲等失效模式。结合先进医学影像与机器人技术以实现自主精准干预的趋势，本文提出了一种统一的CT到仿真流程，用于接触感知的植入规划与验证。我们建立了电极阵列的低维可微分Cosserat杆模型，该模型耦合了摩擦接触与伪动力学正则化，以确保连续的粘滑转换。患者特异性耳蜗解剖结构通过CT影像重建，并借助鼓阶管腔的解析参数化进行编码，从而通过最近点投影实现高效可微分的接触查询。基于微分化的平衡约束公式，我们在类RCM约束下推导了一种在线方向更新律，该律能在维持轴向推进的同时抑制横向插入力。仿真与台架实验验证了变形与力变化趋势，证明其可降低锁定/屈曲风险并提升植入深度。本研究突显了基于CT的影像技术如何增强机器人辅助内耳手术中的建模、规划与安全能力。