Closed‐Loop Haptic–Thermal Perception with Memristor‐Based Spiking Neurons for Embodied Neuromorphic Intelligence
Bibliographic record
Abstract
Abstract Advances in embodied intelligence necessitate the integration of tactile and thermal sensing in artificial sensory systems to enable adaptive human–robot interactions, which compensate for insufficient or entirely unavailable visual information in contact‐haptic operations. Here, a closed‐loop haptic–thermal perception system featuring silver nanowire (AgNW) memristors with dual‐mode pressure‐temperature sensors is presented. Optimized via spin‐coating AgNWs and ALD‐grown Al 2 O 3 encapsulation, AgNW memristors demonstrate bidirectional threshold switching behavior with ultralow leakage current (<1 nA), sub‐1V threshold voltage, and ambient stability. Flexible dual‐mode sensors convert external stimuli into electrical signals, mimicking the human skin's perception of pressure and temperature. Sensory stimuli are processed by AgNW memristor‐based spiking neurons, which can fuse simulated information from dual‐mode sensors into a spike sequence and classify via convolutional neural networks (CNNs), emulating four haptic–thermal perceptual levels in a robot hand—from gentle touch to extreme discomfort. This architecture enables energy‐efficient, low‐latency decision‐making that facilitates artificial nociceptive reflexes for safe human–robot interaction while advancing neuromorphic devices for next‐generation wearable electronics and embodied intelligence.
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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".