Overcoming adhesion forces: Active release of micro objects in micromanipulation
Bibliographic record
Abstract
Due to force scaling laws, rapid, accurate release of micro objects has been a long-standing challenge for microrobotic manipulation. This paper presents an active release technique that for the first time, achieves 100% repeatability and a release accuracy of 0.70plusmn0.46mum, experimentally quantified through the manipulation of 10mum glass spheres under an optical microscope. Using a new MEMS (microelectromechanical systems) microgripper, this technique employs a controllable plunging mechanism for the micro object to gain sufficient momentum to overcome adhesion forces. Experimental results also confirmed that this technique is not substrate dependent. Theoretical analyses were conducted to understand the release principle. Based on this preliminary study, the technique may also prove to be an effective solution to active release of sub-micron objects in robotic pick-place.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".