Wrinkle-Free Sewing with Robotics: The Future of Soft Material Manufacturing
Bibliographic record
Abstract
Abstract Sewing flexible materials such as textiles and clothing can be challenging due to their tendency to wrinkle easily and their non-linear mechanical behaviour. Conventional methods in industrial plants are performed by workers and can be labour-intensive and time-consuming. Therefore, the interest in robotic solutions has grown in the last decade. In this paper, we propose a flexible and reliable robotic solution that can autonomously remove wrinkles from fabric. This method was designed as a part of a robotic cell capable of sewing together two different textiles, used in the manufacturing of cyclist garments. The robotic system employs two compliant soft fingers to stretch the fabric and a vision system to identify the wrinkles to flatten. The design of the fingers is bio-inspired, mimicking the adaptability and dexterity of biological systems, hence improving the gripping performance while reducing the risk of damage to the fabric. The developed vision system performs instance segmentation to identify the wrinkles on the fabric, and then identifies the best places to apply the gripper to flatten the tissue. This two-step process is iterated until wrinkles on the surface do not affect the final sewn product. Such a methodology is highly flexible and has no hard requirements, as the vision system requires only an RGB camera, and the fingers are 3D-printed, an affordable and common manufacturing process. Consequently, the system proposed in this paper can be easily employed in a wide variety of industrial scenarios, improving the productivity and the welfare of the workers.
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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 | 0.001 | 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".