Human-centric robotic manipulation in construction: generative adversarial networks based physiological computing mechanism to enable robots to perceive workers’ cognitive load
Bibliographic record
Abstract
With the recent advancements in sensing technologies, mechatronics, and artificial intelligence, collaborative robots are deployed on construction sites to assist workers in performing physically demanding tasks. However, the human–robot collaboration (HRC) can bring several occupational challenges to workers, ranging from physical collisions to adverse psychological impacts. To date, most of the literature on HRC has focused on addressing physical safety challenges, while very few have considered the psychological safety of the workers. To bridge this gap, by integrating generative adversarial network, autoencoder, machine learning, and robot adaptation techniques, this study proposes a novel physiological computing system that enables the collaborative robot to efficiently perceive workers’ psychological states and regulate its performance seamlessly. The results showed that the proposed system allowed the robot to adjust its performance as per workers’ cognitive load level with 89.6% accuracy. The findings revealed the potential of the proposed system in facilitating safe HRC in construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".