<br />

- **Description**:

The robot plays with 3 complex scenes: a grill with many cooking objects like
toaster, pan, etc. It has to pick, open, place, close. It has to set a table,
move plates, cups, utensils. And it has to place dishes in the sink, dishwasher,
hand cups etc.

- **Homepage** :
  <https://play-fusion.github.io/>

- **Source code** :
  [`tfds.robotics.rtx.CmuPlayFusion`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/robotics/rtx/rtx.py)

- **Versions**:

  - **`0.1.0`** (default): Initial release.
- **Download size** : `Unknown size`

- **Dataset size** : `6.68 GiB`

- **Auto-cached**
  ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
  No

- **Splits**:

| Split | Examples |
|---|---|
| `'train'` | 576 |

- **Feature structure**:

    FeaturesDict({
        'episode_metadata': FeaturesDict({
            'file_path': Text(shape=(), dtype=string),
        }),
        'steps': Dataset({
            'action': Tensor(shape=(9,), dtype=float32, description=Robot action, consists of [7x delta eef (pos + quat), 1x gripper open/close (binary), 1x terminate episode].),
            'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),
            'is_first': bool,
            'is_last': bool,
            'is_terminal': bool,
            'language_embedding': Tensor(shape=(512,), dtype=float32, description=Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5),
            'language_instruction': Text(shape=(), dtype=string),
            'observation': FeaturesDict({
                'image': Image(shape=(128, 128, 3), dtype=uint8, description=Main camera RGB observation.),
                'state': Tensor(shape=(8,), dtype=float32, description=Robot state, consists of [7x robot joint angles, 1x gripper position.),
            }),
            'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
        }),
    })

- **Feature documentation**:

| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
|   | FeaturesDict |   |   |   |
| episode_metadata | FeaturesDict |   |   |   |
| episode_metadata/file_path | Text |   | string | Path to the original data file. |
| steps | Dataset |   |   |   |
| steps/action | Tensor | (9,) | float32 | Robot action, consists of \[7x delta eef (pos + quat), 1x gripper open/close (binary), 1x terminate episode\]. |
| steps/discount | Scalar |   | float32 | Discount if provided, default to 1. |
| steps/is_first | Tensor |   | bool |   |
| steps/is_last | Tensor |   | bool |   |
| steps/is_terminal | Tensor |   | bool |   |
| steps/language_embedding | Tensor | (512,) | float32 | Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5 |
| steps/language_instruction | Text |   | string | Language Instruction. |
| steps/observation | FeaturesDict |   |   |   |
| steps/observation/image | Image | (128, 128, 3) | uint8 | Main camera RGB observation. |
| steps/observation/state | Tensor | (8,) | float32 | Robot state, consists of \[7x robot joint angles, 1x gripper position. |
| steps/reward | Scalar |   | float32 | Reward if provided, 1 on final step for demos. |

- **Supervised keys** (See
  [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):
  `None`

- **Figure**
  ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):
  Not supported.

- **Examples**
  ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):

<iframe src="https:///frame/datasets/catalog/cmu_play_fusion_41d15d556d4d7c7fdee8a438bbb15027812c66a13731852fab846ee0756fd8ee.frame" class="framebox inherit-locale " allow="clipboard-write https://" allowfullscreen is-upgraded></iframe>

- **Citation**:

    @inproceedings{chen2023playfusion,
      title={PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play},
      author={Chen, Lili and Bahl, Shikhar and Pathak, Deepak},
      booktitle={CoRL},
      year={2023}
    }