The Dynamic Mortise-and-Tenon Interlock Assists Hydrated Soft Robots Toward Off-Road Locomotion
Bibliographic record
Abstract
Natural locomotion such as walking, crawling, and swimming relies on spatially controlled deformation of soft tissues, which could allow efficient interaction with the external environment. As one of the ideal candidates for biomimetic materials, hydrogels can exhibit versatile bionic morphings. However, it remains an enormous challenge to transfer these in situ deformations to locomotion, particularly above complex terrains. Herein, inspired by the crawling mode of inchworms, an isotropic hydrogel with thermoresponsiveness could evolve to an anisotropic hydrogel actuator via interfacial diffusion polymerization, further evolving to multisection structure and exhibiting adaptive deformation with diverse degrees of freedom. Therefore, a dynamic mortise-and-tenon interlock could be generated through the interaction between the self-deformation of the hydrogel actuator and rough terrains, inducing continual multidimensional locomotion on various artificial rough substrates and natural sandy terrain. Interestingly, benefiting from the powerful mechanical energy transfer capability, the crawlable hydrogel actuators could also be utilized as hydrogel motors to activate static cargos to overstep complex terrains, which exhibit the potential application of a biomimetic mechanical discoloration device. Therefore, we believe that this design principle and control strategy may be of potential interest to the field of deformable materials, soft robots, and biomimetic devices.
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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 | 0.000 |
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 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".