Conductive fur sensing for a gesture-aware furry robot
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advances in artificial intelligence suggest that machines will soon be capable of communicating in ways previously considered out of their reach. For example, humans engage in sophisticated emotional communication through the language of touch. What technical capabilities would enable computers to do the same? As our group examines this question in the context of emotional touch between a person and a furry social robot, we require sensors designed to detect and recognize subtle, nuanced touches. To this end, we demonstrate a new type of sensor based on conductive fur, which is sensitive to movements unavailable to conventional pressure sensors. The sensor captures motion by measuring changing current as the fur's conductive threads connect and disconnect during touch interaction. We then use machine learning to classify gestures from this time series. An informal evaluation with seven participants found 82% recognition of a 3-gesture set, showing promise for this approach to gesture recognition, and opening a path to emotionally intelligent touch sensing.
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.
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) | 0.004 | 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 it