A Soft Gripper with Rigidity Tunable Elastomer Strips as Ligaments
Bibliographic record
Abstract
Like their natural counterparts, soft bioinspired robots capable of actively tuning their mechanical rigidity can rapidly transition between a broad range of motor tasks-from lifting heavy loads to dexterous manipulation of delicate objects. Reversible rigidity tuning also enables soft robot actuators to reroute their internal loading and alter their mode of deformation in response to intrinsic activation. In this study, we demonstrate this principle with a three-fingered pneumatic gripper that contains "programmable" ligaments that change stiffness when activated with electrical current. The ligaments are composed of a conductive, thermoplastic elastomer composite that reversibly softens under resistive heating. Depending on which ligaments are activated, the gripper will bend inward to pick up an object, bend laterally to twist it, and bend outward to release it. All of the gripper motions are generated with a single pneumatic source of pressure. An activation-deactivation cycle can be completed within 15 s. The ability to incorporate electrically programmable ligaments in a pneumatic or hydraulic actuator has the potential to enhance versatility and reduce dependency on tubing and valves.
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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".