Soft Robotic Hand for Sushi Grasping and Handling
Bibliographic record
Abstract
In this research, a soft robotic hand design was proposed for handling food products, specifically for making and transferring sushi, nigiri, or gunkan. The PneuNet-based design of fingers is made from silicon to minimize damaging the sushi upon grasping. For placing a raw fish slice on top of the rice of a nigiri efficiently, a suction hole was added onto the palm. To ensure safe contact between the robot palm and the sushi for gentle and smooth grasping, picking, and handling, a distance sensors was also installed at the palm area. Thanks to such technical features, the sushi is not deformable or damaged for the best quality. To prove the effectiveness of our soft robotic hand design, a series of experiments were conducted with eight different types of nigiri and sushi. The results show that overall successful rate of grasping is over 90 percentages, which is potential for real-world applications, not only with sushi but also other types of soft and fragile products.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".