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SITR Dataset
This repository hosts the dataset for the Sensor-Invariant Tactile Representation (SITR) paper. The dataset supports training and evaluating models for sensor-invariant tactile representations across simulated and real-world settings. The codebase implementing SITR is available on GitHub: SITR Codebase
For more details on the underlying methods and experiments, please visit our project website and read the arXiv paper.
Dataset Overview
The SITR dataset consists of three main parts:
Simulated Tactile Dataset
A large-scale synthetic dataset generated using physics-based rendering (PBR) in Blender. This dataset spans 100 unique simulated sensor configurations with tactile signals, calibration images, and corresponding surface normal maps. It includes 10K unique contact configurations generated using 50 high-resolution 3D meshes of common household objects, resulting in a pre-training dataset of 1M samples.Classification Tactile Dataset
Data collected from 7 real sensors (including variations of GelSight Mini, GelSight Hex, GelSight Wedge, and DIGIT). For the classification task, 20 objects are pressed against each sensor at various poses and depths, accumulating 1K tactile images per object (140K images in total, with 20K per sensor). We decided to only use 16 of the objects for our classification experiments and some of the items were deemed unsuitable (this was decided before experimentation). The dataset is provided as separate train (80%) and test sets (20%).Pose Estimation Tactile Dataset
For pose estimation, tactile signals are recorded using a modified Ender-3 Pro 3D printer equipped with 3D-printed indenters. This setup provides accurate ground truth (x, y, z coordinates) for contact points. Data were collected for 6 indenters across 4 sensors, resulting in 1K samples per indentor (24K images in total, 6K per sensor). This dataset is also organized into train (80%) and test sets (20%).
Download and Setup
Simulated Tactile Dataset
The simulated dataset is split into two parts due to its size:
renders_part_aa.zip
renders_part_ab.zip
You should be able to download both files with
wget https://huggingface.co/datasets/hgupt3/sitr_dataset/resolve/main/renders_part_aa https://huggingface.co/datasets/hgupt3/sitr_dataset/resolve/main/renders_part_ab
To merge and unzip:
Merge the parts into a single zip file:
cat renders_part_aa renders_part_ab > renders.zip
You can remove the old binaries
rm renders_part_aa renders_part_ab
Unzip the merged file:
unzip renders.zip -d your_desired_directory
Real-World Datasets (Classification & Pose Estimation)
You can download the classificaiton dataset with
wget https://huggingface.co/datasets/hgupt3/sitr_dataset/resolve/main/classification_dataset.zip
and the pose estimation datset with
wget https://huggingface.co/datasets/hgupt3/sitr_dataset/resolve/main/pose_dataset.zip
Simply unzip them in your desired directory:
unzip classification_dataset.zip -d your_desired_directory
unzip pose_dataset.zip -d your_desired_directory
The real-world tactile datasets for classification and pose estimation are provided as separate zip files. Each of these zip files contains two directories:
train_set/
test_set/
File Structure
Below are examples of the directory trees for each dataset type.
1. Simulated Tactile Dataset
data_root/
βββ sensor_0000/
β βββ calibration/ # Calibration images
β β βββ 0000.png # Background image
β β βββ 0001.png
β β βββ ...
β βββ samples/ # Tactile sample images
β β βββ 0000.png
β β βββ 0001.png
β β βββ ...
β βββ dmaps/ # (Optional) Depth maps
β β βββ 0000.npy
β β βββ ...
β βββ norms/ # (Optional) Surface normals
β βββ 0000.npy
β βββ ...
βββ sensor_0001/
βββ ...
2. Classification Dataset
Each of the train_set/
and test_set/
directories follows this structure:
train_set/ (or test_set/)
βββ sensor_0000/
β βββ calibration/ # Calibration images
β βββ samples/ # Organized by class
β β βββ class_0000/
β β β βββ 0000.png
β β β βββ ...
β β βββ class_0001/
β β β βββ 0000.png
β β β βββ ...
β β βββ ...
βββ sensor_0001/
βββ ...
3. Pose Estimation Dataset
Similarly, each of the train_set/
and test_set/
directories is structured as follows:
train_set/ (or test_set/)
βββ sensor_0000/
β βββ calibration/ # Calibration images
β βββ samples/ # Tactile sample images
β β βββ 0000.png
β β βββ 0001.png
β β βββ ...
β βββ locations/ # Pose/Location data
β βββ 0000.npy
β βββ 0001.npy
β βββ ...
βββ sensor_0001/
βββ ...
Citation
If you use this dataset in your research, please cite:
@misc{gupta2025sensorinvarianttactilerepresentation,
title={Sensor-Invariant Tactile Representation},
author={Harsh Gupta and Yuchen Mo and Shengmiao Jin and Wenzhen Yuan},
year={2025},
eprint={2502.19638},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2502.19638},
}
License
This dataset is licensed under the MIT License. See the LICENSE file for details.
If you have any questions or need further clarification, please feel free to reach out.
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