imperialcollege_sawyer_wrist_cam
Stay organized with collections
Save and categorize content based on your preferences.
Sawyer performing table top manipulation
Split |
Examples |
'train' |
170 |
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
}),
'steps': Dataset({
'action': Tensor(shape=(8,), dtype=float32, description=Robot action, consists of 3x delta position in EEF frame, 3x delta ZYX euler angles, 1x gripper open/close, 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=(64, 64, 3), dtype=uint8, description=Main camera RGB observation (same as wrist in our case).),
'state': Tensor(shape=(1,), dtype=float32, description=Gripper state (opened or closed)),
'wrist_image': Image(shape=(64, 64, 3), dtype=uint8, description=Wrist camera RGB observation.),
}),
'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),
}),
})
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 |
(8,) |
float32 |
Robot action, consists of 3x delta position in EEF frame, 3x delta ZYX euler angles, 1x gripper open/close, 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 |
(64, 64, 3) |
uint8 |
Main camera RGB observation (same as wrist in our case). |
steps/observation/state |
Tensor |
(1,) |
float32 |
Gripper state (opened or closed) |
steps/observation/wrist_image |
Image |
(64, 64, 3) |
uint8 |
Wrist camera RGB observation. |
steps/reward |
Scalar |
|
float32 |
Reward if provided, 1 on final step for demos. |
--
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2024-09-03 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 2024-09-03 UTC."],[],[],null,["# imperialcollege_sawyer_wrist_cam\n\n\u003cbr /\u003e\n\n- **Description**:\n\nSawyer performing table top manipulation\n\n- **Homepage** : [--](/datasets/catalog/--)\n\n- **Source code** :\n [`tfds.robotics.rtx.ImperialcollegeSawyerWristCam`](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** : `81.87 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|-----------|----------|\n| `'train'` | 170 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'episode_metadata': FeaturesDict({\n 'file_path': Text(shape=(), dtype=string),\n }),\n 'steps': Dataset({\n 'action': Tensor(shape=(8,), dtype=float32, description=Robot action, consists of 3x delta position in EEF frame, 3x delta ZYX euler angles, 1x gripper open/close, 1x terminate episode.),\n 'discount': Scalar(shape=(), dtype=float32, description=Discount if provided, default to 1.),\n 'is_first': bool,\n 'is_last': bool,\n 'is_terminal': bool,\n 'language_embedding': Tensor(shape=(512,), dtype=float32, description=Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5),\n 'language_instruction': Text(shape=(), dtype=string),\n 'observation': FeaturesDict({\n 'image': Image(shape=(64, 64, 3), dtype=uint8, description=Main camera RGB observation (same as wrist in our case).),\n 'state': Tensor(shape=(1,), dtype=float32, description=Gripper state (opened or closed)),\n 'wrist_image': Image(shape=(64, 64, 3), dtype=uint8, description=Wrist camera RGB observation.),\n }),\n 'reward': Scalar(shape=(), dtype=float32, description=Reward if provided, 1 on final step for demos.),\n }),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|-------------------------------|--------------|-------------|---------|-----------------------------------------------------------------------------------------------------------------------------------|\n| | FeaturesDict | | | |\n| episode_metadata | FeaturesDict | | | |\n| episode_metadata/file_path | Text | | string | Path to the original data file. |\n| steps | Dataset | | | |\n| steps/action | Tensor | (8,) | float32 | Robot action, consists of 3x delta position in EEF frame, 3x delta ZYX euler angles, 1x gripper open/close, 1x terminate episode. |\n| steps/discount | Scalar | | float32 | Discount if provided, default to 1. |\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 | Kona language embedding. See \u003chttps://tfhub.dev/google/universal-sentence-encoder-large/5\u003e |\n| steps/language_instruction | Text | | string | Language Instruction. |\n| steps/observation | FeaturesDict | | | |\n| steps/observation/image | Image | (64, 64, 3) | uint8 | Main camera RGB observation (same as wrist in our case). |\n| steps/observation/state | Tensor | (1,) | float32 | Gripper state (opened or closed) |\n| steps/observation/wrist_image | Image | (64, 64, 3) | uint8 | Wrist camera RGB observation. |\n| steps/reward | Scalar | | float32 | Reward if provided, 1 on final step for demos. |\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 --"]]