tokyo_u_lsmo_converted_externally_to_rlds
Stay organized with collections
Save and categorize content based on your preferences.
motion planning trajectory of pick place tasks
Split |
Examples |
'train' |
50 |
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
}),
'steps': Dataset({
'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x endeffector position, 3x euler angles,1x gripper action].),
'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=(120, 120, 3), dtype=uint8, description=Main camera RGB observation.),
'state': Tensor(shape=(13,), dtype=float32, description=Robot state, consists of [3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position].),
}),
'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 |
(7,) |
float32 |
Robot action, consists of [3x endeffector position, 3x euler angles,1x gripper action]. |
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 |
(120, 120, 3) |
uint8 |
Main camera RGB observation. |
steps/observation/state |
Tensor |
(13,) |
float32 |
Robot state, consists of [3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position]. |
steps/reward |
Scalar |
|
float32 |
Reward if provided, 1 on final step for demos. |
@Article{Osa22,
author = {Takayuki Osa},
journal = {The International Journal of Robotics Research},
title = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization},
year = {2022},
number = {3},
pages = {291--311},
volume = {41},
}
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 2024-12-11 UTC.
[null,null,["Last updated 2024-12-11 UTC."],[],[],null,["# tokyo_u_lsmo_converted_externally_to_rlds\n\n\u003cbr /\u003e\n\n- **Description**:\n\nmotion planning trajectory of pick place tasks\n\n- **Homepage** :\n \u003chttps://journals.sagepub.com/doi/full/10.1177/02783649211044405\u003e\n\n- **Source code** :\n [`tfds.robotics.rtx.TokyoULsmoConvertedExternallyToRlds`](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** : `335.71 MiB`\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| `'train'` | 50 |\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=(7,), dtype=float32, description=Robot action, consists of [3x endeffector position, 3x euler angles,1x gripper action].),\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=(120, 120, 3), dtype=uint8, description=Main camera RGB observation.),\n 'state': Tensor(shape=(13,), dtype=float32, description=Robot state, consists of [3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position].),\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 | (7,) | float32 | Robot action, consists of \\[3x endeffector position, 3x euler angles,1x gripper action\\]. |\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 | (120, 120, 3) | uint8 | Main camera RGB observation. |\n| steps/observation/state | Tensor | (13,) | float32 | Robot state, consists of \\[3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position\\]. |\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 @Article{Osa22,\n author = {Takayuki Osa},\n journal = {The International Journal of Robotics Research},\n title = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization},\n year = {2022},\n number = {3},\n pages = {291--311},\n volume = {41},\n }"]]