bridge
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WidowX interacting with toy kitchens
Split |
Examples |
'test' |
3,475 |
'train' |
25,460 |
FeaturesDict({
'episode_metadata': FeaturesDict({
'episode_id': Scalar(shape=(), dtype=int32),
'file_path': string,
'has_image_0': Scalar(shape=(), dtype=bool),
'has_image_1': Scalar(shape=(), dtype=bool),
'has_image_2': Scalar(shape=(), dtype=bool),
'has_image_3': Scalar(shape=(), dtype=bool),
'has_language': Scalar(shape=(), dtype=bool),
}),
'steps': Dataset({
'action': Tensor(shape=(7,), dtype=float32),
'discount': Scalar(shape=(), dtype=float32),
'is_first': bool,
'is_last': bool,
'is_terminal': bool,
'language_embedding': Tensor(shape=(512,), dtype=float32),
'language_instruction': string,
'observation': FeaturesDict({
'image_0': Image(shape=(256, 256, 3), dtype=uint8),
'image_1': Image(shape=(256, 256, 3), dtype=uint8),
'image_2': Image(shape=(256, 256, 3), dtype=uint8),
'image_3': Image(shape=(256, 256, 3), dtype=uint8),
'state': Tensor(shape=(7,), dtype=float32),
}),
'reward': Scalar(shape=(), dtype=float32),
}),
})
Feature |
Class |
Shape |
Dtype |
Description |
|
FeaturesDict |
|
|
|
episode_metadata |
FeaturesDict |
|
|
|
episode_metadata/episode_id |
Scalar |
|
int32 |
|
episode_metadata/file_path |
Tensor |
|
string |
|
episode_metadata/has_image_0 |
Scalar |
|
bool |
|
episode_metadata/has_image_1 |
Scalar |
|
bool |
|
episode_metadata/has_image_2 |
Scalar |
|
bool |
|
episode_metadata/has_image_3 |
Scalar |
|
bool |
|
episode_metadata/has_language |
Scalar |
|
bool |
|
steps |
Dataset |
|
|
|
steps/action |
Tensor |
(7,) |
float32 |
|
steps/discount |
Scalar |
|
float32 |
|
steps/is_first |
Tensor |
|
bool |
|
steps/is_last |
Tensor |
|
bool |
|
steps/is_terminal |
Tensor |
|
bool |
|
steps/language_embedding |
Tensor |
(512,) |
float32 |
|
steps/language_instruction |
Tensor |
|
string |
|
steps/observation |
FeaturesDict |
|
|
|
steps/observation/image_0 |
Image |
(256, 256, 3) |
uint8 |
|
steps/observation/image_1 |
Image |
(256, 256, 3) |
uint8 |
|
steps/observation/image_2 |
Image |
(256, 256, 3) |
uint8 |
|
steps/observation/image_3 |
Image |
(256, 256, 3) |
uint8 |
|
steps/observation/state |
Tensor |
(7,) |
float32 |
|
steps/reward |
Scalar |
|
float32 |
|
@inproceedings{walke2023bridgedata,
title={BridgeData V2: A Dataset for Robot Learning at Scale},
author={Walke, Homer and Black, Kevin and Lee, Abraham and Kim, Moo Jin and Du, Max and Zheng, Chongyi and Zhao, Tony and Hansen-Estruch, Philippe and Vuong, Quan and He, Andre and Myers, Vivek and Fang, Kuan and Finn, Chelsea and Levine, Sergey},
booktitle={Conference on Robot Learning (CoRL)},
year={2023}
}
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Last updated 2024-05-16 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-05-16 UTC."],[],[],null,["# bridge\n\n\u003cbr /\u003e\n\n- **Description**:\n\nWidowX interacting with toy kitchens\n\n- **Homepage** :\n \u003chttps://rail-berkeley.github.io/bridgedata/\u003e\n\n- **Source code** :\n [`tfds.robotics.rtx.Bridge`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/robotics/rtx/rtx.py)\n\n- **Versions**:\n\n - **`0.1.0`** (default): Initial release.\n- **Download size** : `Unknown size`\n\n- **Dataset size** : `387.49 GiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n No\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'test'` | 3,475 |\n| `'train'` | 25,460 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'episode_metadata': FeaturesDict({\n 'episode_id': Scalar(shape=(), dtype=int32),\n 'file_path': string,\n 'has_image_0': Scalar(shape=(), dtype=bool),\n 'has_image_1': Scalar(shape=(), dtype=bool),\n 'has_image_2': Scalar(shape=(), dtype=bool),\n 'has_image_3': Scalar(shape=(), dtype=bool),\n 'has_language': Scalar(shape=(), dtype=bool),\n }),\n 'steps': Dataset({\n 'action': Tensor(shape=(7,), dtype=float32),\n 'discount': Scalar(shape=(), dtype=float32),\n 'is_first': bool,\n 'is_last': bool,\n 'is_terminal': bool,\n 'language_embedding': Tensor(shape=(512,), dtype=float32),\n 'language_instruction': string,\n 'observation': FeaturesDict({\n 'image_0': Image(shape=(256, 256, 3), dtype=uint8),\n 'image_1': Image(shape=(256, 256, 3), dtype=uint8),\n 'image_2': Image(shape=(256, 256, 3), dtype=uint8),\n 'image_3': Image(shape=(256, 256, 3), dtype=uint8),\n 'state': Tensor(shape=(7,), dtype=float32),\n }),\n 'reward': Scalar(shape=(), dtype=float32),\n }),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|-------------------------------|--------------|---------------|---------|-------------|\n| | FeaturesDict | | | |\n| episode_metadata | FeaturesDict | | | |\n| episode_metadata/episode_id | Scalar | | int32 | |\n| episode_metadata/file_path | Tensor | | string | |\n| episode_metadata/has_image_0 | Scalar | | bool | |\n| episode_metadata/has_image_1 | Scalar | | bool | |\n| episode_metadata/has_image_2 | Scalar | | bool | |\n| episode_metadata/has_image_3 | Scalar | | bool | |\n| episode_metadata/has_language | Scalar | | bool | |\n| steps | Dataset | | | |\n| steps/action | Tensor | (7,) | float32 | |\n| steps/discount | Scalar | | float32 | |\n| steps/is_first | Tensor | | bool | |\n| steps/is_last | Tensor | | bool | |\n| steps/is_terminal | Tensor | | bool | |\n| steps/language_embedding | Tensor | (512,) | float32 | |\n| steps/language_instruction | Tensor | | string | |\n| steps/observation | FeaturesDict | | | |\n| steps/observation/image_0 | Image | (256, 256, 3) | uint8 | |\n| steps/observation/image_1 | Image | (256, 256, 3) | uint8 | |\n| steps/observation/image_2 | Image | (256, 256, 3) | uint8 | |\n| steps/observation/image_3 | Image | (256, 256, 3) | uint8 | |\n| steps/observation/state | Tensor | (7,) | float32 | |\n| steps/reward | Scalar | | float32 | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{walke2023bridgedata,\n title={BridgeData V2: A Dataset for Robot Learning at Scale},\n author={Walke, Homer and Black, Kevin and Lee, Abraham and Kim, Moo Jin and Du, Max and Zheng, Chongyi and Zhao, Tony and Hansen-Estruch, Philippe and Vuong, Quan and He, Andre and Myers, Vivek and Fang, Kuan and Finn, Chelsea and Levine, Sergey},\n booktitle={Conference on Robot Learning (CoRL)},\n year={2023}\n }"]]