Dynamic Tactile Data of Textures On Uneven Surfaces
Bibliographic record
Abstract
This dataset provides tactile sensor data captured using an a multi-modal tactile sensing (BioIn-Tacto [1, 2]) module mounted on the end-effector of a OpenManipulatorX. It includes barometric and MARG (Magnetic, Angular Rate, and Gravity) data to support research in texture recognition and robotic manipulation. The data was collected as the manipulator moved the sensing module across 12 different textures applied to a concave/convex surface. The data is organized into folders representing various stages of processing: Data/ ├── 0_Raw ├── 1_Merged ├── 2_Trimmed ├── 3_Normalized └── 4_Windowed - 0_Raw: Raw data for 12 textures (T1 to T12), with 25 exploratory movements per texture. - 1_Merged: Merged barometric and IMU data. - 2_Trimmed: Preprocessed and trimmed data. - 3_Normalized: Normalized data for consistency. - 4_Windowed: Data segmented into windows for analysis. Each exploratory movement contains the following files: - `baro.csv`: Barometric data. - `imus.csv`: IMU data (acceleration, angular rate, and magnetic field). The `Scripts` folder includes tools for automating data preprocessing: Scripts/ ├── merge.py ├── trim.py ├── normalize.py ├── window_creator.py ├── npy_creator.py └── run.sh - merge.py: Merges barometric and IMU data. - trim.py: Trims data based on predefined points. - normalize.py: Normalizes data for consistency. - window_creator.py: Segments data into windows. - npy_creator.py: Converts CSV data into `.npy` format for machine learning. - run.sh: Automates the entire data preprocessing pipeline. It sequentially runs the following steps: 1. Merges barometric and IMU data from the raw data directory (`merge.py`). 2. Trims the merged data based on predefined points stored in `trim_points.json` (`trim.py`). 3. Normalizes the trimmed data for consistency (`normalize.py`). 4. Segments the normalized data into windows of varying sizes (`window_creator.py`). 5. Converts the windowed data into `.npy` format for machine learning models with window sizes of 128, 256, and 512 (`npy_creator.py`). [1] T. E. Alves de Oliveira, A. -M. Cretu and E. M. Petriu, "Multimodal Bio-Inspired Tactile Sensing Module," in IEEE Sensors Journal, vol. 17, no. 11, pp. 3231-3243, 1 June1, 2017, https://doi.org/10.1109/JSEN.2017.2690898. [2] T. E. Alves de Oliveira, V. Prado da Fonseca, BioIn-Tacto: A compliant multi-modal tactile sensing module for robotic tasks, HardwareX, Volume 16, 2023, e00478, ISSN 2468-0672, https://doi.org/10.1016/j.ohx.2023.e00478.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".