Robotic Rotational Positioning of End-Effectors for Micromanipulation
Bibliographic record
Abstract
Precise rotational positioning of end-effectors under microscopy is crucial for robotic micromanipulation. However, the end-effector is presently limited to a fixed orientation, which is manually set before a given micromanipulation task, lacking accuracy and versatility ofin situreorientation. In this article, we present a unified framework for rotationally positioning the end-effector in three dimensions by establishing a general rotational model, developing a detection method within the limited field of view under microscopy, and designing a three-loop control strategy that adapts to different experimental requirements and model parameters. In experiments, a standard angled micropipette was used as the end-effector to verify the validity of the proposed methods. The performance was evaluated experimentally where the micropipette was robotically rotated to an arbitrarily desired orientation with an average orientation error less than$2^\circ$. In the experiments of sperm manipulation, thein situmicropipette orientation control capability improved the success rate of sperm immobilization and achieved dexterous robotic sperm orientation for facile aspiration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".