Magnetically Controlled Soft Robotics Utilizing Elastomers and Gels in Actuation: A Review
Bibliographic record
Abstract
A magnetic field has unique advantages in controlling soft robotics inside of an enclosed space, such as surgical catheters or untethered drug‐delivering robots operating in the human body. Soft actuators, made of elastomers and gels functionalized with magnetically active materials, are natural choices to drive magnetically controlled motions of soft robots. Recent innovations in soft material technologies, including 3D printing, origami/kirigami, tough hydrogels, mechanical metamaterials, and liquid metal‐injected elastomers, offer technological foundations to develop soft actuators and robots with significantly enhanced performance. Herein, an overview of magnetic soft actuators and robots from a materials engineer's perspective is provided. First, the historical background and recent trends of magnetic soft actuators are discussed. Second, the motions of tethered or untethered magnetic soft robotics are classified into aquatic swimmers, terrestrial locomotors, and grippers. Herein, preprogrammed motion under patterned magnetic stimuli is achieved by controlled magnetization of elastomeric materials containing hard magnetic particles. Finally, the applications of magnetically controlled soft robotics in surgical and therapeutic medical devices are discussed.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".