Mobile robotic rebar cage assembly via imitation learning
Bibliographic record
Abstract
Manipulation remains a key bottleneck in achieving fully autonomous rebar cage assembly. Existing solutions based on rail-guided systems are expensive, poorly scalable, and limited in capability. This paper introduces a framework that leverages a mobile manipulator and uses visual servoing together with imitation learning (IL) to address complex rebar manipulation tasks. The framework enables autonomous execution of two challenging manipulation tasks: (a) tight-fit rebar slot insertion and (b) rebar tying at complex intersection nodes within cages. Using only low-cost RGB cameras, the proposed approach achieves over 90% success rate for over 20 rollouts on both tasks. A highlight is the integration of a segmentation module and a reinsertion strategy that improves tight-fit insertion performance by 41.7% over the baseline and significantly improves robustness to background changes. Notably, the system requires neither depth sensors nor explicit geometric modeling, and supports rapid deployment in novel environments. This paper establishes a foundation for extending autonomy to broader rebar manipulation scenarios. Qualitative results are available on the project website 1 1 https://rebarbot.github.io . . • Integrates imitation learning and visual servoing for autonomous rebar cage assembly. • Achieves the first tight-fit insertion of rebars into assembly slots. • Enables rebar tying at complex intersection nodes with only RGB cameras. • Improves success rate and robustness over a baseline imitation learning rebar policy.
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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 | 0.001 |
| 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".