Automated End-Effector Alignment for Robotic Cell Manipulation
Bibliographic record
Abstract
Cell manipulation is a key technology in many biomedical and clinical applications, in which end-effector alignment is a critical procedure. Presently, end-effector alignment is performed manually and suffers from large misalignment error and inconsistency. Manual alignment often undesirably moves the end-effector (e.g., a glass micropipette) out of the limited field of view under microscopy and risks breaking the fragile end-effector. This paper presents automated end-effector alignment for robotic cell manipulation. A rotational degree of freedom was added to a micromanipulator with translational degrees of freedom. The kinematic model of end-effector’s rotation was established, and the unknown model parameters were calibrated and updated via quadratic optimization. A controller was designed based on the kinematics modeling and parameter optimization to compensate for rotation-induced translation and achieve end-effector alignment. Experimental results demonstrate that the robotic alignment technique achieved an accuracy of 0.6±0.3° and a time cost of 18.5 ± 10.2 s, both significantly less than manual alignment. The developed controller cost significantly less time for micropipette alignment than the PID controller. A glass micropipette was used as the end-effector for human sperm immobilization, a critical procedure in clinical cell surgery. The success rate of sperm immobilization was 97% by robotic micropipette alignment, higher than the success rate of 90% by manual alignment due to the higher accuracy of robotic alignment.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".