berkeley_mvp_converted_externally_to_rlds
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xArm performing 6 manipulation tasks
Split |
Examples |
'train' |
480 |
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
}),
'steps': Dataset({
'action': Tensor(shape=(8,), dtype=float32, description=Robot action, consists of [7 delta joint pos,1x gripper binary state].),
'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({
'gripper': Scalar(shape=(), dtype=bool, description=Binary gripper state (1 - closed, 0 - open)),
'hand_image': Image(shape=(480, 640, 3), dtype=uint8, description=Hand camera RGB observation.),
'joint_pos': Tensor(shape=(7,), dtype=float32, description=xArm joint positions (7 DoF).),
'pose': Tensor(shape=(7,), dtype=float32, description=Gripper pose, robot frame, [3 position, 4 rotation]),
}),
'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 [7 delta joint pos,1x gripper binary state]. |
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/gripper |
Scalar |
|
bool |
Binary gripper state (1 - closed, 0 - open) |
steps/observation/hand_image |
Image |
(480, 640, 3) |
uint8 |
Hand camera RGB observation. |
steps/observation/joint_pos |
Tensor |
(7,) |
float32 |
xArm joint positions (7 DoF). |
steps/observation/pose |
Tensor |
(7,) |
float32 |
Gripper pose, robot frame, [3 position, 4 rotation] |
steps/reward |
Scalar |
|
float32 |
Reward if provided, 1 on final step for demos. |
@InProceedings{Radosavovic2022,
title = {Real-World Robot Learning with Masked Visual Pre-training},
author = {Ilija Radosavovic and Tete Xiao and Stephen James and Pieter Abbeel and Jitendra Malik and Trevor Darrell},
booktitle = {CoRL},
year = {2022}
}
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Last updated 2024-09-03 UTC.
[null,null,["Last updated 2024-09-03 UTC."],[],[],null,["# berkeley_mvp_converted_externally_to_rlds\n\n\u003cbr /\u003e\n\n- **Description**:\n\nxArm performing 6 manipulation tasks\n\n- **Homepage** :\n \u003chttps://arxiv.org/abs/2203.06173\u003e\n\n- **Source code** :\n [`tfds.robotics.rtx.BerkeleyMvpConvertedExternallyToRlds`](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** : `12.34 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| `'train'` | 480 |\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 [7 delta joint pos,1x gripper binary state].),\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 'gripper': Scalar(shape=(), dtype=bool, description=Binary gripper state (1 - closed, 0 - open)),\n 'hand_image': Image(shape=(480, 640, 3), dtype=uint8, description=Hand camera RGB observation.),\n 'joint_pos': Tensor(shape=(7,), dtype=float32, description=xArm joint positions (7 DoF).),\n 'pose': Tensor(shape=(7,), dtype=float32, description=Gripper pose, robot frame, [3 position, 4 rotation]),\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 \\[7 delta joint pos,1x gripper binary state\\]. |\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/gripper | Scalar | | bool | Binary gripper state (1 - closed, 0 - open) |\n| steps/observation/hand_image | Image | (480, 640, 3) | uint8 | Hand camera RGB observation. |\n| steps/observation/joint_pos | Tensor | (7,) | float32 | xArm joint positions (7 DoF). |\n| steps/observation/pose | Tensor | (7,) | float32 | Gripper pose, robot frame, \\[3 position, 4 rotation\\] |\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 @InProceedings{Radosavovic2022,\n title = {Real-World Robot Learning with Masked Visual Pre-training},\n author = {Ilija Radosavovic and Tete Xiao and Stephen James and Pieter Abbeel and Jitendra Malik and Trevor Darrell},\n booktitle = {CoRL},\n year = {2022}\n }"]]