Active control of stiffness in soft robotics with piezoresistive strain sensor
Bibliographic record
Abstract
This research delves into an innovative approach to layer jamming, where the jamming structure serves a dual purpose: jamming and pressure sensing. The tunable stiffness technique empowers soft grippers to manage both their grip strength and rigidity. To bolster stiffness control, a piezoresistive sensor is integrated, monitoring changes in resistance during gripping operations. This is achieved by stacking multiple thin layers of MWCNT-polyurethane acrylate material within an elastomer envelope, forming the gripper's jamming structure and pressure sensor. The sensor fabrication combines Direct Ink Writing and Digital Light Projection printing methods, while the gripper itself is crafted from Polydimethylsiloxane and Ecoflex. Utilizing resistance value shifts from the sensor, we optimize gripping performance. Experimental validations confirm the efficacy of the sensing and layer jamming approaches. Precise gripper stiffness control is achieved through data gleaned from the sensor's force measurements. Furthermore, this study illuminates potential applications in healthcare and the wearable technology industry.
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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) | 0.000 | 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) | 0.000 | 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".