Impedance-Modulated Soft Strain Sensor with High Stability for Humanoid Robots
Bibliographic record
Abstract
Soft strain sensors are crucial for enabling humanoid robots to perform industrial, medical, and other human-related tasks. However, the limited internal space of humanoid robots exposes soft strain sensors to interference from line resistance, contact resistance, and alternating magnetic fields generated by motor actuator systems and power conversion circuits. To address this issue, inspired by biological neural signal systems, this work proposes an impedance-modulated soft strain sensor with high stability. The sensor combines a liquid metal (LM) resistor, a capacitor, and an inductor to form a passive band-stop filter, which is encapsulated in a soft elastomer. Tensile strain increases the resistance of the LM resistor, reducing the impedance of the sensor at resonance and converting the resistance signal into an impedance-modulated signal. Based on filter theory, the circuit structure of the sensor and the selection of the resistance, capacitance, and inductance components are analyzed in terms of stability, sensitivity, and measurement feasibility. By employing a series connection of resistance and inductance, the sensor achieves high impedance at resonance, effectively suppressing interference from line and contact resistance. Additionally, the frequency of the impedance-modulated sensor does not overlap with the frequency of electromagnetic interference (EMI) from the humanoid robot motor drivers, achieving immunity to EMI. Furthermore, a Field Programmable Gate Array-based signal acquisition system is constructed to measure the impedance-modulated signal. Finally, an application of this impedance-modulated sensor in humanoid robots and wearable devices is demonstrated.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".