---
annotations_creators: []
language: en
size_categories:
- 100K<n<1M
task_categories:
- robotics
task_ids: []
pretty_name: aloha_pen_uncap
tags:
- fiftyone
- group
- LeRobot
dataset_summary: >




  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 146,800
  Samples samples.


  ## Installation


  If you haven't already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  from fiftyone.utils.huggingface import load_from_hub


  # Load the dataset

  # Note: other available arguments include 'max_samples', etc

  dataset = load_from_hub("Voxel51/aloha_pen_uncap")


  # Launch the App

  session = fo.launch_app(dataset)

  ```
license: apache-2.0
---

# Dataset Card for aloha_pen_uncap

![image/png](aloha-uncap-fo-hq.gif)

This dataset is a [FiftyOne](https://github.com/voxel51/fiftyone) conversion in LeRobot format of the `aloha_pen_uncap_diverse` subset of BiPlay.


The **aloha_pen_uncap_diverse** subset is a task-specific segment of BiPlay focusing on the long-horizon, dexterous bimanual task of un-capping a pen under diverse conditions. It contains episodes where the robot is required to grasp a pen and successfully remove its cap—an action requiring coordination and dexterity—across a wide range of object placements, backgrounds, and distractor objects. This diversity is designed specifically to benchmark policy generalization and to test the ability of learned policies (such as diffusion transformer-based ones) to adapt to varied real-world scenarios[4][5].

Key attributes of the **aloha_pen_uncap_diverse** subset:

- **Task:** Bimanual pen uncapping with an ALOHA robot, including significant variation in scene and object arrangement.
- **Format:** Converted into the LeRobot dataset v2.0 format for compatibility with common robotics learning frameworks[6][4].
- **Data Contents:** The dataset includes state sequences, action sequences, velocities, efforts, and high-resolution images from multiple camera viewpoints for each time step.
- **Research Use:** Commonly used to benchmark methods such as Diffusion Transformer Policies (DiT-Policy), which aim for robust, generalizable robotic manipulation through large-scale, language-annotated data[3][7].



## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("Voxel51/aloha_pen_uncap")

# Launch the App
session = fo.launch_app(dataset)
```


### Dataset Sources

• Paper: https://huggingface.co/papers/2410.10088 

• Code: https://github.com/sudeepdasari/dit-policy 

Learn more about converting LeRobot format datasets into FiftyOne format: https://github.com/harpreetsahota204/fiftyone_lerobot_importer



### Citation

```bibtex
@inproceedings{dasari2025ingredients,
  title={The Ingredients for Robotic Diffusion Transformers},
  author={Sudeep Dasari and Oier Mees and Sebastian Zhao and Mohan Kumar Srirama and Sergey Levine},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)},
  year={2025},
  address = {Atlanta, USA}
}
```