---
annotations_creators:
- expert-generated
language_creators:
- n/a
language:
- n/a
license:
- cc-by-4.0
multilinguality:
- n/a
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- other
dataset_info:
  features:
  - name: terrain_id
    dtype: int32
  - name: composition
    dtype: string
  - name: material_1
    dtype: string
  - name: material_2
    dtype: string
  - name: material_3
    dtype: string
  - name: sample_index
    dtype: int32
  - name: rgb_image
    dtype: image
  - name: depth_image
    dtype: image
  - name: depth_min
    dtype: float32
  - name: depth_max
    dtype: float32
  - name: ft_csv_path
    dtype: string
  - name: pixel_x
    dtype: float32
  - name: pixel_y
    dtype: float32
  - name: yaw
    dtype: float32
  - name: scoop_depth
    dtype: float32
  - name: stiffness
    dtype: float32
  - name: scooped_volume
    dtype: float32
  splits:
  - name: train
    num_bytes: 0
    num_examples: 0
  download_size: 0
  dataset_size: 0
pretty_name: Scooping Dataset
dataset_description: |
  The Scooping Dataset contains 6,700 samples collected over 67 terrains for the task of manipulating granular materials using a robotic arm. The dataset facilitates research in robotic manipulation, machine learning, and related fields.
homepage: https://drillaway.github.io/
repository: https://github.com/pthangeda/scooping-dataset
citation: |
  @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}
  }
---

# 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']
```

### Loading All Data for a Specific Terrain ID

If you want to load all samples corresponding to a specific terrain ID, you can filter the dataset using the `filter` method. Here's how you can do it:

```python
# Specify the terrain ID you're interested in
terrain_id_of_interest = 10  # Replace with the desired terrain ID (1-67)

# Filter the dataset to include only samples from the specified terrain
terrain_samples = dataset.filter(lambda sample: sample['terrain_id'] == terrain_id_of_interest)

print(f"Number of samples for terrain {terrain_id_of_interest}: {len(terrain_samples)}")
for sample in terrain_samples:
    # Load RGB image
    rgb_image = sample['rgb_image']  # PIL Image

    # Load and reconstruct depth image
    depth_image = sample['depth_image']  # PIL Image
    depth_array = np.array(depth_image).astype(np.float32)
    depth_normalized = depth_array / 65535
    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']

    # Perform your analysis or processing here
    # Example: Print scooped volume for each sample
    print(f"Sample {sample['sample_index']}: Scooped volume = {scooped_volume} cubic meters")
```

## 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/).

---