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oai:data.mendeley.com/yctpcddnm7.2 2026-09-24T23:31:52Z

Nazario Coelho, Mateus Tactile Data of 12 Textures on Uneven Surfaces Collected with a 6-DoF Robotic Arm Mendeley Data This dataset provides tactile data captured with the BioIn-Tacto multimodal tactile sensing module [1, 2] mounted on the end-effector of a Lite6 robotic arm [3]. 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. The ROS_Bags folder includes the bags from each texture, and the STL folder includes the concave convex .stl shape. [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. [3] Ltd. Shenzhen UFACTORY Co., UFACTORY Lite 6 User Manual, (n.d.). https://static.generation-robots.com/media/ufactory-lite6-user-manual.pdf (accessed August 10, 2026). Robotics Microstructural Texture Tactile Perception Surface Texture Time Series Measurement in Robotics Khatibi, Soheil Martins de Sousa, Frederico Luiz Prado da Fonseca, Vinicius Alves de Oliveira, Thiago Eustaquio Dataset doi:10.17632/yctpcddnm7.2 oai:data.mendeley.com/yctpcddnm7.2 Creative Commons Attribution 4.0 International http://creativecommons.org/licenses/by/4.0 https://data.mendeley.com/datasets/yctpcddnm7 2026-09-24T23:31:52Z