Rebar grasp detection using a synthetic model generator and domain randomization
Bibliographic record
Abstract
The increasing demand for automated rebar cage assembly in the construction industry highlights the need for flexible rebar grasping solutions. This paper proposes a grasp detection method that enables robotic arms to autonomously grasp rebars from the top layer of stacks, eliminating the need for complex delivery systems. To support this, a synthetic dataset pipeline incorporating domain randomization is developed, which facilitates robust rebar instance segmentation without the need for labor-intensive real-world data collection. Within this pipeline, a fully-parameterized rebar generator is proposed to eliminate the reliance on manual modeling in data generation, allowing an infinite generation of rebar datasets with realistic and diverse appearances and shapes. Real-world experiments demonstrated a segmentation accuracy of 87.9 for rebars in the top layer and a 91.6 % grasping success rate on the first attempt, validating the proposed methods. Additionally, an ablation study highlighted the significance of rebar stacking, lighting, and camera pose variations in improving the model performance in real-world scenarios.
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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".