Bibliographic record
Abstract
Soft robots outperform the conventional robots on enhanced safety for human–machine interaction, environmental adaptability, and continuous deformation. In this blooming area of fundamental and technological importance, liquid crystal polymer networks and liquid crystal elastomers (referred to as LCNs) have emerged as one of the most valuable candidates for soft robots due to their complex, large, and reversible shape change capabilities. To date, much research effort, mainly regarding chemical synthesis, fabrication technologies and soft robot design, has been dedicated to LCN robotic systems to endow them with versatile and complex actions controlled by various stimuli. Herein, starting with the principle that governs the stimuli‐responsiveness of LCNs, recent progress made in LCN soft robots is summarized while focusing on different robotic motions, such as grapping, walking, swimming, and oscillation. Especially, novel LCNs with intelligent functions such as reprocessability, reconfigurability, self‐regulating behavior and associative learning capability, are highlighted. This article aims to provide significant insights into the design and development of LCN‐based soft robots.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".