Extending feeling past the e-skin surface: adding proximity sensing to a soft capacitive force sensor
Bibliographic record
Abstract
A focus on ‘human-like’ robotics over the past decade has resulted in a wide variety of novel soft sensors, commonly called ‘E-skins’. Skin-like sensors which sense single- or multi-axis force are common, but the inclusion of alternative modalities such as proximity have yet to be thoroughly explored. The addition of proximity to the inside of a robotic hand may enhance the ability to perceive object position and shape for grip. In this work, proximity sensing is incorporated into a three-axis capacitive E-Skin force sensor intended for use on a robotic hand. This sensor is fabricated out of Ecoflex 00-30© with carbon nanoparticle-infused elastomer used to form conductive elements and has a 14 x 14 x 2.5 mm3 total volume without disrupting the underlying sensor functionality; the best-performing design achieved a maximum effective object detection distance of 25.2 mm. The functionality of these conformable proximity sensor designs was also demonstrated on curved surfaces mimicking those expected in E-skin application. The designs were observed to maintain general function down to curvatures of 20 mm inner radius with a 23-69% reduction in maximum signal change compared to mounting on a flat surface but no other significant change to overall approach signal curvature. Each proximity sensor was also demonstrated to act as a sufficient capacitive shield for the underlying tactile sensor when switched to a grounding mode, allowing for a thinner overall multimodal sensor with proximity and 3-axis force detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".