用少量标注实现显微镜下机器人操作的高精度辅助。
Learning From a Steady Hand: A Weakly Supervised Agent for Robot Assistance under Microscopy
- 通过预热轨迹提取隐式空间信息，无需外部标记或手动标深度。
- 横向定位误差达49微米（95%置信度），深度误差小于291微米。
- 用户实验显示工作负荷降低77.1%，适合临床显微干预场景。
本文重新思考稳手机器人操作，提出一种弱监督框架，融合校准感知视觉与阻抗控制。不同于依赖繁琐2D标注的传统自动化方法，该框架利用可复用的预热轨迹提取隐式空间信息，实现无需外部标志物或人工深度标注的校准感知、深度分辨感知。通过显式刻画观测与校准模型间的残差，系统从记录的预热数据中建立任务空间误差预算。不确定性预算下，横向闭环精度达约49微米（95%置信度，最坏情况测试子集），大平面移动时深度精度≤291微米（95%置信度）。在8名受试者的组内用户研究中，所学代理相较简单稳手辅助基线，总体NASA-TLX工作负荷降低77.1%。结果表明，该弱监督代理在不增加复杂设置的前提下，提升了显微镜引导生物医学微操作的可靠性，为显微镜引导干预提供实用框架。
原文摘要 · Abstract (English)
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.
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