A multimodal tactile dataset for dynamic texture classification
Bibliographic record
Abstract
Reproducing human-like dexterous manipulation in robots requires identifying objects and textures. In unstructured settings, robots equipped with tactile sensors may detect textures by using touch-related characteristics. An extensive dataset of the physical interaction between a tactile-enable robotic probe is required to investigate and develop methods for categorizing textures. Therefore, this motivates us to compose a dataset from the signals of a bioinspired multimodal tactile sensing module while a robotic probe brings the module to dynamically contact 12 tactile textures under three exploratory velocities. This dataset contains pressure, acceleration, angular rate, and magnetic field variation signals from sensors embedded in the compliant structure of the sensing module. The pressure signals were sampled at 350 Hz, while the signals of the other sensors were sampled at 1500 Hz. Each texture was explored 100 times for each exploratory velocity, and each exploratory episode consisted of a sliding motion in the x and y directions tangential to the surface where the texture is placed. The total number of exploratory episodes in the dataset is 3600. The tactile texture dataset can be used for any project in the area of object recognition and robotic manipulation, and it is especially well suited for tactile texture reconstruction and recognition tasks. The dataset can also be used to study anisotropic textures and how robotic tactile exploration has to consider sliding motion directions.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.012 |
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".