This paper introduces a learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, providing a compact representation of deformation. A data-driven model was trained on 5,097 experimental samples to learn the mapping from magnetic field parameters (magnitude: 0-14 mT, frequency: 0.2-1.0 Hz, vertical tip distances: 90-100 mm) to Bezier control points defining the robot's 3D shape. Both Neural Network (NN) and Random Forest (RF) architectures were compared. The RF model outperformed the NN, achieving a mean RMSE of 0.087 mm in control point prediction and 0.064 mm in shape reconstruction error. Feature importance analysis revealed that magnetic field components predominantly influence distal control points, while frequency and distance affect the base configuration. Unlike prior studies applying general machine learning to soft robotic data, this framework introduces a new paradigm linking magnetic actuation inputs directly to geometric Bezier control points, creating an interpretable, low-dimensional deformation representation. This integration of magnetic field characterization, embedded FBG sensing, and Bezier-based learning provides a unified strategy extensible to other magnetically actuated continuum robots. By enabling sub-millimeter shape prediction and real-time inference, this work advances intelligent control of magnetically actuated soft robotic tools in minimally invasive neurosurgery.

翻译：本文提出了一种基于学习的建模框架，用于设计用于内窥镜鼻内脑肿瘤切除的磁控柔性吸引装置。该装置实现了微型化（外径4毫米，内径2毫米，长度40毫米），采用生物相容性SIL 30材料通过3D打印制造，并集成了嵌入式光纤布拉格光栅传感器以实现实时形状反馈。形状重建采用四个贝塞尔控制点表示，提供了紧凑的变形表征。基于5,097个实验样本训练了一个数据驱动模型，以学习从磁场参数（强度：0-14 mT，频率：0.2-1.0 Hz，末端垂直距离：90-100毫米）到定义机器人三维形状的贝塞尔控制点的映射关系。研究比较了神经网络和随机森林两种架构。随机森林模型表现优于神经网络，在控制点预测中实现了0.087毫米的平均均方根误差，在形状重建误差中实现了0.064毫米。特征重要性分析表明，磁场分量主要影响远端控制点，而频率和距离则影响基座配置。与先前将通用机器学习应用于软体机器人数据的研究不同，该框架引入了一种新范式，将磁驱动输入直接关联到几何贝塞尔控制点，从而创建了一种可解释的低维变形表征。这种磁场表征、嵌入式光纤布拉格光栅传感和基于贝塞尔的学习方法的整合，提供了一种可扩展至其他磁驱动连续体机器人的统一策略。通过实现亚毫米级形状预测和实时推理，本研究推动了微创神经外科中磁驱动软体机器人工具的智能控制发展。