IIR Filter Models of Haptic Vibration Textures
Bibliographic record
Abstract
Haptic tactile feedback is a widely used and effective technique in virtual reality applications. When an object's surface is explored by stroking it using fingers, finger nails, or a tool, a vibration response is sensed. The vibrations convey information about the surface finish and patterns in the surface structure, and they may help identify the surface. We study characteristics of real-world physical objects that are based on actual measurements. We propose novel techniques for modeling haptic vibration textures using digital filters that can simulate both stochastic and patterned textures of objects. Modeling is based on a spatial distribution of infinite-impulse-response filters that operate in the time domain. We match the impulse response of the filters to acceleration profiles that are obtained from scanning of real-world objects. The results show that our modeling is efficient in representing varying roughness characteristics of both regular-patterned and stochastic surfaces unlike prior methods that are based on a parametric decaying sinusoidal model. Our experiments employ an existing handheld mobile scanning setup with a visually tracked probe, which provides acceleration and force profiles. Our simple capturing devices also remove any need for a robotic manipulator.
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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".