Evaluation and selection of workstations for an application of Human-Robot-Interaction (HRI) in manufacturing: Presentation held at IROS 2017, Fiendly People, Friendly Robots, Vancouver, September 24 - 28, 2017
Bibliographic record
Abstract
Direct interaction between human and robot provides multiple benefits in manufacturing. In 2016, ISO/TS 15066 has been published as the first document describing safety regulation and operation modes of collaborative robots. It has motivated numerous manufacturing companies and especially SME to think about using HRI. However, a clear methodology to find the optimal combination between human’s and robot’s competences in various workstations is still unavailable. The main objective is to define which workstations are suitable and how they can be designed for an optimal HRI solution. This methodology takes into account the individual requirements and the future visions of the manufacturing companies starting from space and time limitations, passing by ergonomics and requirements as flexibility. The proposed methodology has been tested in various case studies within five companies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".