Remarks from an experimental study on human-robot collaborative assembly
Bibliographic record
Abstract
Human robot collaboration is becoming the norm in the workplace, due to the benefits robots can bring to efficiency and production. However, this creates highly complex and dynamic workplaces that human operators need to adapt to. Industry 5.0 promotes the use of robotics and smart technologies in a more human-centric way. However, research on how operators are affected by those changes is needed to better understand how to move towards human-centricity. As such, an experimental study was designed and performed on human robot collaborative assembly. The main aim was to investigate the correlation between cognitive load and quality due to collaboration. Here, the preliminary results of the experimental study are presented in order to remark relevant states influencing work allocation. The results showcased the need for better training and more knowledge for the operators, as well as involving operators in process and workplace design. This study helps contribute knowledge on robot implementation and process design for human robot collaboration for both researchers and operations management, as it showcases the need to involve operators in those steps due to the feedback they can provide due to their experience.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".