Programmable Shape‐Preserving Soft Robotics Arm via Multimodal Multistability
Bibliographic record
Abstract
Abstract Inflatable multistable materials have significantly advanced the design of shape‐preserving soft robotic arms, offering substantial benefits in terms of shape adaptability, energy efficiency, and safety, ensuring operational reliability even in the event of sudden power loss. However, existing strategies for realizing multistable arms often limit themselves to a single mode of multistability, commonly with rotationally symmetric designs favoring extension stability and asymmetric designs inducing bending stability. To address the limitation, this study introduces a pioneering platform termed multimodal multistability that utilizes geometrical frustration. A single cylindrical symmetric cell, designed for extension bistability, could achieve frustrated multistable states in bending by controlling the cell with multiple degrees of freedom incorporated pneumatic actuator. This platform extends the spectrum of attainable stable trajectories while preserving essential attributes of arms, such as load‐bearability, programmability, and reversibility of shape changes. Leveraging a pneumatic system with four degrees of freedom for pressure control, not only enables capturing previously unexplored stable configurations in mechanical metastructures but also allows for the control of their deformation modes. With applications spanning space exploration, medical instruments, and rescue missions, the multimodal multistability promises unparalleled flexibility and efficiency in the design and operation of 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".