Vision-Based 2-D Automatic Micrograsping Using Coarse-to-Fine Grasping Strategy
Bibliographic record
Abstract
In this paper, we propose a visual-servo-control approach and a two-stage grasping strategy and, then, develop control software to perform micrograsping tasks, i.e., to control a passive microgripper to automatically grasp a micropart, in a 2-D plane with high accuracy. In the proposed control scheme, we employ closed-loop control with the use of two position feedback signals: relative positions of the micropart with respect to the microgripper measured by the vision-control system and absolute displacements of the micropart measured by linear encoders. To improve the grasping efficiency and success rate, a two-stage grasping strategy is employed: (1) the bonded microgripper is controlled to directly reach a specific position adjacent to the mating edge of a designated micropart with the same y coordinate, by matching the patterns of the microgripper and the micropart only once, and (2) finely align the micropart with the microgripper along the x and y translation axes of the microassembly robot in the horizontal plane by employing the proposed visual servo control, until the micropart is completely grasped. Experiments conducted with a 6-DOF microassembly robot demonstrate the efficiency and validity of the proposed control approach and grasping strategy.
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".