Multi-material Fabrication for Magnetically Driven Miniature Soft Robots Using Stereolithography
Bibliographic record
Abstract
Remote manipulation and controlled navigation of magnetically driven miniature soft robots make them promising robotic tools operating in hard-to-reach workspace. The functionality of robots can be enhanced by integrating multiple materials with different mechanical or magnetic characteristics. However, it remains challenging combining multiple materials along with arbitrary magnetization profile formation during fabrication. This study, from a pixel level, uses stereolithography process to precisely incorporate multiple materials with different physical properties for millimeter-scale robot printing, as well as encode discrete magnetizations for the actuating parts, which provides a customizable approach for sophisticated shape production. Complex shape transformations and dynamic motions were observed through the magnetic actuation of printed robots. With the integration of magnetoactive and non-magnetic materials, free locomotion in a liquid environment tracked by optical and ultrasonic detections was achieved by actuating a 4-arm flapping robot. Moreover, discrete patterns were formed with the combination of soft and rigid magnetic materials. Such versatility of robotic behaviors and enhanced morphing capabilities enable the creation of complex multi-material actuators and provide a promising route towards a wide spectrum of biomedical applications.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".