Vision-Based 2-D Automatic Micrograsping Using Coarse-to-Fine Grasping Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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