Elevating immersive construction training with high-fidelity haptic feedback for human–robot collaboration
Bibliographic record
Abstract
The integration of robotics in construction enhances productivity, efficiency, and safety. However, effective human–robot collaboration remains a challenge. Immersive human–machine interfaces have been identified as facilitators for improving interaction with robots in such contexts. While these immersive interfaces can provide a promising representation of real-world situations, they typically fall short of delivering the essential interaction fidelity, particularly in tasks that require fine motor control. This study develops and evaluates a high-fidelity haptic feedback system to replicate physical interactions in immersive human–robot collaboration. The study employed both quantitative and qualitative measures, including selective attention, user engagement, perceived usability, and self-efficacy, in a user experiment that involved a bricklaying construction task. The results highlighted the significant enhancement of user experience metrics through the integration of high-fidelity haptic feedback, providing valuable insights for the development of more intuitive and efficient immersive interfaces tailored for human–robot collaboration in construction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".