CONSISTENCY ANALYSIS AND SUGGESTIONS OF COLLISION MEASUREMENT IN HUMAN–ROBOT COLLABORATION SAFETY EVALUATION, 1-13.
Bibliographic record
Abstract
Collaborative robot has the advantages of human-robot collaboration (HRC), being cost-effective and flexible to deploy, and having wide application prospect.Operators may have expected or unexpected contact with the robot during collaboration, which brings potential risks caused by collision.Due to the complexity of collision measurement and the absence of sophisticated standards, huge controversy on what a collaborative robot is safe occurs.We first point out that there are two long-standing issues in collision measurements that cannot be completely solved in a short time.Motivated by the pressing safety needs of a fast-growing collaborative robot industry, it is now increasingly urgent to ensure consistency of collision measurements to avoid controversy.Based on the current achievements, an overall methodology for collision testing is summarised and key technical issues are identified.Influencing factors of human-robot collision measurement are first analysed systematically and discussed thoroughly.Corresponding suggestions are proposed to ensure the result consistency of collision measurement, having great practical value and broad promotion potentiality.This fills the gap between the insufficient safety standards and the urgent need of collaborative robot safety evaluation.Moreover, suggestions for standards in the future will provide strong support for collaborative robot safety.
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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 | 0.063 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".