A Soft Needle Gripper Capable of Grasping and Piercing for Handling Food Materials
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
Automation in the food industry is not as developed as in the automotive industry because of difficulties in handling food products with large variations in shape, size, and mechanical properties. In this paper, a pneumatic-driven soft needle gripper is proposed for handling food materials. It was constructed using a soft membrane and multiple rigid needle-like fingers. It can work under two operational modes: grasping and piercing. The grasping mode can be used to grasp shredded and chopped food materials. The piercing mode is for handling food products when only the top surface of the food product is available for handling. The needle gripper is fabricated using a multi-material 3D printer. Experimental tests on various food materials are conducted to validate grasping and piercing operations. The results of grasping tests suggest that the needle gripper can successfully grasp shredded and chopped food materials. A quantitative analysis shows that the relative standard deviation of the grasped food weight was within 20%. Although the results of piercing tests validate that the needle gripper can successfully handle various food materials, releasing the food materials involves certain difficulties.
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