Tactile Data of 12 Textures on Uneven Surfaces Collected with a 6-DoF Robotic Arm

It includes barometric and MARG (Magnetic, Angular Rate, and Gravity) data to support research in texture recognition and robotic manipulation, collected as the manipulator moved the sensing module across 12 textures applied to concave/convex surfaces. Data/ ├── 0_Raw: Raw CSVs extracted from ROS bags, organized by shape/texture(T1–T12)/experiment (25 movements per texture). ├── 1_Merged: Raw per-sensor CSVs merged into a single time-aligned CSV per experiment. ├── 2_Trimmed: Recordings trimmed to keep only the valid interaction segment. ├── 3_Normalized: Trimmed signals normalized (scaled) per experiment, producing normalized_data.csv files. ├── 4_Windowed: Signals split into sliding windows of 128, 256, and 512 samples. ├── 5_Reindexed: Windows reindexed and merged for consistent numbering across experiments. ├── 6_NPY: Final .npy arrays per dataset and window size, plus windows_per_experiment.json metadata. └── 7_Folded: Windows organized into cross-validation folds for train/test evaluation.

Each exploratory movement contains baro.csv (barometric data) and imus.csv (IMU data: acceleration, angular rate, and magnetic field). The Scripts folder includes tools for automating data preprocessing: Scripts/ ├── merge_datasets.sh: Runs merge.py per shape to build 1_Merged. ├── trim_dataset.sh: Trims recordings; supports --create_json_only / --use_json modes and per-texture or preview runs. ├── normalize_datasets.sh: Runs normalize.py on every trimmed CSV to build 3_Normalized. ├── create_windows.sh: Runs window_creator.py per dataset/surface to generate the 128/256/512-sample windows. ├── reindex_windows.sh: Reindexes and merges windows into 6_Reindexed. ├── create_npy.sh: Runs npy_creator.py per dataset and window size to export .npy files. ├── generate_windows_metadata.sh: Writes per-dataset windows_per_experiment.json files. ├── fold_generator.py: Creates the balanced 2, 4, and 5-Fold cross-validation splits from the windowed .npy data. └── run.sh: Automates the entire preprocessing pipeline by sequentially running the previous steps.