Add scooping dataset

.gitattributes

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 # Video files - compressed
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+*.csv filter=lfs diff=lfs merge=lfs -text

README.md

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+# Scooping Dataset
+
+This dataset contains 6,700 samples collected over 67 terrains for the task of manipulating granular materials using a robotic arm. The dataset is designed to facilitate research in robotic manipulation, machine learning, and related fields. Each sample includes:
+
+- **Terrain Metadata**: Terrain ID, composition, and materials used.
+- **RGB Image**: An RGB image of the terrain before scooping.
+- **Depth Image**: Depth data corresponding to the terrain saved as a 16-bit PNG image.
+- **Depth Normalization Parameters**: `depth_min` and `depth_max` to reconstruct original depth values.
+- **F/T Sensor Data**: Force/Torque sensor data captured during the scooping action, saved as a CSV file.
+- **Action Parameters**: Scoop location, yaw angle, scoop depth, and stiffness.
+- **Outcome**: Volume of material scooped.
+
+## Dataset Structure
+
+### Features
+
+- `terrain_id` (`int32`): Terrain identifier (1-67).
+- `composition` (`string`): Terrain composition (`single`, `partition`, `mixture`, `layers`).
+- `material_1` (`string`): First material used in the terrain.
+- `material_2` (`string`): Second material used in the terrain (empty string if not applicable).
+- `material_3` (`string`): Third material used in the terrain (empty string if not applicable).
+- `sample_index` (`int32`): Sample index within the terrain (1-100).
+- `rgb_image` (`Image`): RGB image of the terrain before scooping.
+- `depth_image` (`Image`): Depth data as a 16-bit PNG image.
+- `depth_min` (`float32`): Minimum depth value used for normalization.
+- `depth_max` (`float32`): Maximum depth value used for normalization.
+- `ft_csv_path` (`string`): File path to the F/T sensor data (CSV file).
+- `pixel_x` (`float32`): X-coordinate of the scoop location in the image.
+- `pixel_y` (`float32`): Y-coordinate of the scoop location in the image.
+- `yaw` (`float32`): Yaw angle of the end-effector (in radians).
+- `scoop_depth` (`float32`): Depth of the scoop (in meters).
+- `stiffness` (`float32`): Stiffness of the controller during the scoop action.
+- `scooped_volume` (`float32`): Volume of material scooped (in cubic meters).
+
+### Data Files
+
+The dataset is organized with the following structure:
+
+```
+scooping_dataset/
+├── scooping_dataset.arrow
+├── dataset_info.json
+├── rgb_images/
+│   ├── terrain_1_sample_1_rgb.png
+│   ├── terrain_1_sample_2_rgb.png
+│   └── ...
+├── depth_images/
+│   ├── terrain_1_sample_1_depth.png
+│   ├── terrain_1_sample_2_depth.png
+│   └── ...
+├── ft_data/
+│   ├── terrain_1_sample_1_ft.csv
+│   ├── terrain_1_sample_2_ft.csv
+│   └── ...
+```
+
+## Usage
+
+To use this dataset, you can load it using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_from_disk
+import numpy as np
+from PIL import Image as PILImage
+import pandas as pd
+
+# Load the dataset
+dataset = load_from_disk('path_to_dataset/scooping_dataset')
+
+# Access a sample
+sample = dataset[0]
+
+# Load RGB image
+rgb_image = sample['rgb_image']  # PIL Image
+rgb_image.show()
+
+# Load depth image and reconstruct original depth values
+depth_image = sample['depth_image']  # PIL Image
+depth_array = np.array(depth_image).astype(np.float32)
+depth_normalized = depth_array / 65535  # Normalize back to [0, 1]
+depth_min = sample['depth_min']
+depth_max = sample['depth_max']
+original_depth = depth_normalized * (depth_max - depth_min) + depth_min
+
+# Load F/T sensor data
+ft_data = pd.read_csv(sample['ft_csv_path'], header=None).values
+
+# Access action parameters
+pixel_x = sample['pixel_x']
+pixel_y = sample['pixel_y']
+yaw = sample['yaw']
+scoop_depth = sample['scoop_depth']
+stiffness = sample['stiffness']
+scooped_volume = sample['scooped_volume']
+```
+
+## Dataset Creation
+
+The dataset was created using the following steps:
+
+1. **Data Collection**: 6,700 samples were collected using a UR5e robotic arm equipped with a scooping end-effector and an overhead RealSense L515 camera to capture RGB-D images.
+
+2. **Terrain Preparation**: 67 terrains were prepared using combinations of 12 different materials and 4 types of compositions. Each terrain represents a unique task.
+
+3. **Action Execution**: For each terrain, 100 scooping actions were performed with varying action parameters (scoop location, yaw, depth, and stiffness).
+
+4. **Data Recording**: Before each action, an RGB-D image of the terrain was captured. During the action, F/T sensor data was recorded. After the action, the volume of material scooped was measured.
+
+## License
+
+This dataset is licensed under the **Creative Commons Attribution 4.0 International (CC BY 4.0) License**.
+
+[![License: CC BY 4.0](https://licensebuttons.net/l/by/4.0/88x31.png)](https://creativecommons.org/licenses/by/4.0/)
+
+You are free to:
+
+- **Share** — copy and redistribute the material in any medium or format.
+- **Adapt** — remix, transform, and build upon the material for any purpose, even commercially.
+
+Under the following terms:
+
+- **Attribution** — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
+
+**Full License Text**: [https://creativecommons.org/licenses/by/4.0/legalcode](https://creativecommons.org/licenses/by/4.0/legalcode)
+
+## Citation
+
+If you use this dataset, please cite the following paper:
+
+```bibtex
+@inproceedings{Zhu-RSS-23,
+  author    = {Zhu, Yifan and Thangeda, Pranay and Ornik, Melkior and Hauser, Kris},
+  title     = {Few-shot Adaptation for Manipulating Granular Materials Under Domain Shift},
+  booktitle = {Proceedings of Robotics: Science and Systems},
+  year      = {2023},
+  month     = {July},
+  address   = {Daegu, Republic of Korea},
+  doi       = {10.15607/RSS.2023.XIX.048}
+}
+```
+
+## References
+
+For more details and illustrations of the materials and compositions, please visit our [project website](https://drillaway.github.io/).
+
+---
+