Autonomous Robotic Pick-and-Place of Microobjects
Why this work is in the frame
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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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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