Accurate Embedded Force Sensor for Soft Robots with Rate-dependent Deep Neural Calibration
Bibliographic record
Abstract
Embedding force sensors on soft robots have been a major challenge impeding accurate feedback control of soft robots. A major challenge in embedding force sensors onto soft robots is in their rigidity, size, and shape. In this study, a soft smart polymer-based soft sensor for soft robotic application is proposed, prototyped, calibrated, and tested for force prediction accuracy. The sensing element of the proposed sensor was made of gelatin-graphite composite that we previously showed its piezoresistivity. Three sensing elements were molded into a soft body (soft robot) and variation of the voltage across them was measured in real-time in response to external loads. A rate-dependent deep neural calibration network was trained with the three voltages and their temporal rates when the soft body was subjected to tri-axial external forces in the range of$\pm 0.3$N. Afterwards the calibrated sensor was used in a series of validation tests to assess its accuracy. The proposed calibration showed the goodness of fit of$R^{2}=0.98$with the mean-absolute error of 0.005 N. Also, the sensor exhibited mean-absolute errors of$0.007 \pm0.005$N,$0.008 \pm 0.006$N, and$0.011 \pm 0.008 \ \mathbf{N}$for estimating the external forces along x, y, and z directions. Moreover, the proposed calibration did not exhibit observable hysteresis thanks to its rate-dependent calibration schema.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".