Dual-arm micromanipulation and handling of objects through visual images
Bibliographic record
Abstract
Pick-and-place of micro-scale objects is essential for many microscopic tasks. For more sophisticated tasks, grasping and manipulating objects with two independent tools can enhance the capability and the dexterity of the tools. In this work, a dual-arm micromanipulation system equipped with two tungsten probes was employed for the manipulation of a sphere. In order to provide sufficient contact area for grasping, the two probes were positioned side-by-side to grasp a sphere lying on a glass substrate. Visual images were used to provide feedback for manipulating the sphere from one location to the desired location, finally releasing the sphere. Since the adhesion force is dominant in the micro-scale environment, the sphere adheres to the probe and could not be released. To resolve this issue, the two probes were reconfigured after the manipulation. The contact points of the two probes were reconfigured from a side-by-side contact, to a tip-to-tip contact with the sphere. Experimental results confirmed that through this probe reconfiguration, the success rate of sphere release from the probes is higher, allowing improved throughput with such an approach.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".