From Symmetry to Asymmetry: Design Framework of a Waterbomb Origami Soft Crawler for Linear and Steering Locomotion
Bibliographic record
Abstract
Abstract Soft robots have garnered significant attention in recent years due to their adaptability, compliance, and potential for safe interaction with unstructured environments. However, achieving efficient linear and steering locomotion in these systems remains a persistent challenge. Origami, the ancient art of paper folding, offers a promising framework for designing soft robotic systems due to its ability to achieve a large range of deformation by transforming simple 2D sheets into complex 3D structures. While prior research has extensively investigated the behaviors of Waterbomb origami under the fully symmetric condition, efficient actuation methods and sub-symmetric behavior remain understudied. This study addresses this gap by introducing a novel computational model to characterize sub-symmetric folding patterns in Waterbomb origami. By combining these sub-symmetric behaviors with fully symmetric folding, the research leverages geometric asymmetry to design a soft origami crawler capable of locomotion. The proposed model captures the kinematics of the origami structure and facilitates the design of a crawler that achieves both linear and steering locomotion. Experimental results validate the model and demonstrate the crawler’s ability to move efficiently in both modes. These findings underscore the potential of utilizing geometric asymmetry in origami-based soft robots to enable versatile and adaptive locomotion, paving the way for advanced soft robotic systems that can operate in unstructured and dynamic environments.
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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) | 0.001 | 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) | 0.000 | 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".