This paper rethinks steady-hand robotic manipulation by using a weakly supervised framework that fuses calibration-aware perception with admittance control. Unlike conventional automation that relies on labor-intensive 2D labeling, our framework leverages reusable warm-up trajectories to extract implicit spatial information, thereby achieving calibration-aware, depth-resolved perception without the need for external fiducials or manual depth annotation. By explicitly characterizing residuals from observation and calibration models, the system establishes a task-space error budget from recorded warm-ups. The uncertainty budget yields a lateral closed-loop accuracy of approx. 49 micrometers at 95% confidence (worst-case testing subset) and a depth accuracy of <= 291 micrometers at 95% confidence bound during large in-plane moves. In a within-subject user study (N=8), the learned agent reduces overall NASA-TLX workload by 77.1% relative to the simple steady-hand assistance baseline. These results demonstrate that the weakly supervised agent improves the reliability of microscope-guided biomedical micromanipulation without introducing complex setup requirements, offering a practical framework for microscope-guided intervention.

翻译：本文通过采用一种融合标定感知与导纳控制的弱监督框架，重新思考了稳定操作机器人操控问题。与依赖劳动密集型二维标注的传统自动化方法不同，我们的框架利用可重复使用的预热轨迹来提取隐式空间信息，从而在无需外部基准标记或手动深度标注的情况下，实现标定感知的深度解析感知。通过显式表征观测模型与标定模型的残差，系统从记录的预热数据中建立了任务空间误差预算。该不确定性预算在95%置信度下（最坏情况测试子集）实现了约49微米的横向闭环精度，在大范围平面内移动时实现了≤291微米的深度精度（95%置信区间）。在一项受试者内用户研究（N=8）中，学习得到的智能体相较于基础稳定操作辅助基线，将NASA-TLX总体工作负荷降低了77.1%。这些结果表明，弱监督智能体在不引入复杂设置要求的前提下，提高了显微镜引导生物医学微操作的可信性，为显微镜引导介入提供了实用框架。