Contact Detection in Microrobotic Manipulation
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a computer vision-based method for visually detecting the contact between an end-effector and a target surface under an optical microscope during microrobotic manipulation. Without using proximity or force/touch sensors, this method provides a submicrometer detection accuracy and possesses robustness. Fundamentally, after the establishment of contact in the world frame, further vertical motion of the end-effector (flexible or stiff) induces horizontal motion in the image plane. Contact between a micropipette tip and a glass slide in the scenario of microrobotic cell manipulation is used as an example to elaborate on the detection method. Experimental results demonstrate that the computer vision-based method is capable of achieving contact detection between the micropipette and the glass slide surface with an accuracy of 0.2 μm. Furthermore, 1000 experimental trials reveal that the presented method is robust to variations in illumination intensity, microscopy magnification, and microrobot motion speed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it