The Future of Soft Material Assembly: An Intelligent System for Autonomous Sewing Process in Industries
Bibliographic record
Abstract
Abstract Manipulating soft materials remains one of the most challenging tasks in robotics due to their highly nonlinear mechanical behavior. This complexity has significantly hindered automation in industries reliant on soft material manipulation, such as textile manufacturing. In the particular case of cycling garment, production requires precise handling of elastic fabrics and foam pads, which has traditionally relied on skilled manual labor. This paper presents an autonomous robotic system designed to automate these tasks, enhancing efficiency and consistency. The proposed robotic cell integrates needle-based grippers mounted on robotic manipulators to flatten fabrics and handle foam pads, while a Cartesian robot ensures precise material positioning beneath the sewing machine needle. Developed in collaboration with Decathlon, the system is designed to seamlessly integrate into existing production lines, complementing manual workstations rather than replacing them. Experimental results demonstrate that the robotic cell achieves product quality and manufacturing times comparable to those of skilled human operators. Furthermore, the modular architecture of the system allows easy adaptation to various fabric types, garment designs, and production requirements. This work represents a significant step toward the automation of complex textile manufacturing processes, increasing productivity while at the same time reducing physical strain on workers and improving overall working conditions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".