VAIRO: A Vision-Based Adaptive Impedance-Control Robotic Framework
Bibliographic record
Abstract
In this work, we present VAIRO, a Vision-based Adaptive Impedance-control RObotic framework for the purpose of manipulating soft materials, centered around the use case of rolling croissant dough for use in artisanal bakeries. Traditional automated processes for the industrial production of croissants consist of overly bulky equipment and fail to preserve the artisanal quality of hand-rolled croissants, with one of the major challenges being the high variability in the dough properties. VAIRO addresses these challenges by introducing a novel vision-based adaptive Cartesian impedance control strategy for collaborative robot arms to regulate rolling forces in real-time without the need for estimating the properties of the soft material. As such, VAIRO mimics the tactile adjustments made by human pastry chefs, ensuring consistent layer thickness and eliminating gaps. Using a Kinova Gen3 robotic arm and a custom-designed end-effector, we demonstrate that VAIRO can successfully manipulate various "doughs" without estimating any material properties. These results are promising and offer a cost-effective, small-scale alternative for local craft bakeries to leverage automation while maintaining high artisanal quality.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".