Sleeved Bending Actuators for Soft Grippers: A Durable Solution for High Force-to-Weight Applications
Bibliographic record
Abstract
Soft grippers are known for their ability to interact with objects that are fragile, soft or of an unknown shape, as well as humans in collaborative robotics applications. However, state-of-the-art soft grippers lack either payload capacity or durability, which limits their use in industrial applications. In fact, high force density pneumatic soft grippers require high strain and operating pressure, both of which impair their durability. This work presents a new sleeved bending actuator for soft grippers that is capable of high force density and durability. The proposed actuator is based on design principles previously proven to improve the life of pneumatic artificial muscles, where a sleeve provides a uniform reinforcement that reduces local stresses and strains in the inflated membrane. The sleeved bending actuator features a silicone membrane and an external two-material sleeve that can support high pressures while providing a flexible grip. The proposed sleeved bending actuators are validated through two grippers, sized according to foreseen soft gripper applications: A small gripper for drone perching and lightweight food manipulation, and a larger one for the manipulation of heavy material (>5 kg) of various weights and sizes. Performance assessment shows that these grippers have payloads up to 5.2 kg and 20 kg, respectively. Durability testing of the grippers demonstrates that the grippers have an expected lifetime ranging from 263,000 cycles to more than 700,000 cycles. The grippers are tested in various settings, including the integration of a gripper into a Phantom 2 quadcopter, a perching demonstration, as well as the gripping of light and heavy food items. Experiments show that sleeved bending actuators constitute a promising avenue for durable and strong soft grippers.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".