A dataset for tactile textures on uneven surfaces collected using a BioIn-Tacto sensing module
Bibliographic record
Abstract
Effective human-like manipulation in robots depends on their capacity to recognize and identify textures in different environments. In unpredictable environments, robots with tactile sensors will have to identify textures through touch-related features. To advance research in texture classification, a comprehensive dataset capturing the physical interactions between a tactile-enabled robotic probe and various textures is necessary. As a result, we are driven to create a dataset from the signals collected by a bioinspired multimodal tactile sensing module, as a robotic probe dynamically makes contact with 12 different tactile textures. This dataset includes signals for pressure, acceleration, angular rate, and magnetic field variations, all captured by sensors embedded within the flexible structure of the sensing module. The pressure signals and the signals from the other sensors were sampled at a rate of 130 Hz. Each texture was explored 25 times, with each exploration involving a sliding motion along the uneven surface, tangential to the surface where the texture was bonded. The dataset comprises a total of 300 exploratory episodes. The tactile texture dataset applies to various projects in object recognition and robotic manipulation, making it particularly valuable for tasks involving tactile texture reconstruction and recognition. Additionally, this dataset offers opportunities to study time series properties generated by the robotic sliding motions during tactile texture exploration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".