Humanoid Robot Actuation through Precise Chemical Sensing Signals
Bibliographic record
Abstract
Abstract As the need for assistive robots increases in aging societies, various assistive robot systems including humanoid robots have been developed. Humanoid robotic hands are one of the most useful parts to assist humans efficiently. While sensing pressure or temperature from the robotic hands is extensively studied, sensing chemicals is less widely studied despite the significant importance. Here, a unique platform of smartly moving humanoid fingers actuated by chemical sensing is reported. The sensor is printed with disposable and biocompatible cellulose conductive ink materials. R f intensity change of the sensor with NH 4 + membrane depends on NH 4 + ion concentration where R ² is 0.9576. The smart bending motion of a finger is accomplished by logically programed actuation through detecting the change of interested ion concentration from 0.01 to 1 m at the ion‐selective membrane electrode (ISME) sensor with resulted bending angles from 10° to 67° accordingly. The overall signal‐to‐noise ratio is over 10. This sensing robot concept may be expanded to applications of microrobots which receive external stimulus, judge, and execute the actuation to carry out programed tasks.
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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) | 0.002 | 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".