Autonomous Robotic Pick-and-Place of Microobjects
Bibliographic record
Abstract
This paper presents a robotic system that is capable of both picking up and releasing microobjects with high accuracy, reliability, and speed. Due to force-scaling laws, large adhesion forces at the microscale make rapid, accurate release of microobjects a long-standing challenge in micromanipulation, thus representing a hurdle toward automated robotic pick-and-place of micrometer-sized objects. The system employs a novel microelectromechanical systems (MEMS) microgripper with a controllable plunging structure to impact a microobject that gains sufficient momentum to overcome adhesion forces. The performance was experimentally quantified through the manipulation of 7.5-10.9 ¿m borosilicate glass spheres in an ambient environment. Experimental results demonstrate that the system, for the first time, achieves a 100% success rate in release (which is based on 700 trials) and a release accuracy of 0.45 ± 0.24 ¿m. High-speed, automated microrobotic pick-and-place was realized by visually recognizing the microgripper and microspheres, by visually detecting the contact of the microgripper with the substrate, and by vision-based control. Example patterns were constructed through automated microrobotic pick-and-place of microspheres, achieving a speed of 6 s/sphere, which is an order of magnitude faster than the highest speed that has been reported in the literature.
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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 | 0.001 |
| 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".