Robotization of Miniature-Scale Radio-Controlled Excavator: A New Medium for Construction- Specific DNN Data Generation
Bibliographic record
Abstract
Digital management of excavators has seen limited progress due to challenges in artificial intelligence (AI) training. The AI required for digital twinning of excavators necessitates a large volume of diverse imagery data, currently scarce in the construction domain. Moreover, the absence of deployable robot agents hinders reinforcement learning, impeding task-oriented AI development. In response, we introduce an innovative approach utilizing a miniature-scale, radio-controlled excavator (RC-excavator). This presents a cost-effective method for automated data collection and labeling, as well as interactive reinforcement learning. The RC-excavator’s electric circuit was modified, its motion dynamics were modeled, and it was fully robotized for precise computer-directed motion control. Statistical validation of its motions achieved a Normalized Range Adjusted Accuracy (NRAA) of 99.14% for the bucket, 97.97% for the main arm, and 98.63% for the cabin. This confirms its adequacy for image labeling and task-oriented automation research.
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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 | 0.000 | 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 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".