Assembly Complexity Index (ACI) for Modular Robotic Systems: Validation and Conceptual Framework for AR/VR-Assisted Assembly
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The growing adoption of modular robotic systems presents new challenges in ensuring ease of assembly, deployment, and reconfiguration, especially for end-users with varying technical expertise. This study proposes and validates an Assembly Complexity Index (ACI) framework, combining subjective workload (NASA Task Load Index) and task complexity (Task Complexity Index) into a unified metric to quantify assembly difficulty. Twelve participants performed modular manipulator assembly tasks under supervised and unsupervised conditions, enabling evaluation of learning effects and assembly complexity dynamics. Statistical analyses, including Cronbach’s alpha, correlation studies, and paired t-tests, demonstrated the framework’s internal consistency, sensitivity to user learning, and ability to capture workload-performance trade-offs. Additionally, we propose an augmented reality (AR) and virtual reality (VR) integration workflow to further mitigate assembly complexity, offering real-time guidance and adaptive assistance. The proposed framework not only supports design iteration and operator training but also provides a human-centered evaluation methodology applicable to modular robotics deployment in Industry 4.0 environments. The AR/VR-assisted workflow presented here is proposed as a conceptual extension and will be validated in future work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it