Digital Twin-Based Biofeedback Controlling of Human-Cobot Interaction Upon a Manufacturing Application
Bibliographic record
Abstract
Cobots (collaborative robots) are widely exploited in the manufacturing industry as smart assistants in proximity to human operators. The race towards mass automation brought by the fourth industrial revolution has made the safety of humans a widely discussed topic. Industrial guidelines have been introduced to accommodate this change in the manufacturing industry for better use of cobots without compromising human safety. Built-in safety is encouraged to be incorporated from the cobot programming stage itself to facilitate this safe collaborative environment. To achieve that, research is being done to train the cobots with various contact avoidance algorithms. Mitigating productivity loss while the cobots are in these trained safe operating modes, has been identified as a requirement by the researchers to take real advantage of collaborative workspaces. To address this requirement, the authors are proposing a novel cobot-controlling algorithm for human-cobot interaction by considering the biofeedback of the human operator. The proposed algorithm is part of a model workcell development which will be remotely controlled using a digital twin platform.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".