- Description:
wheelchair with arm performing shelf pick tasks
Source code:
tfds.robotics.rtx.DlrEdanSharedControlConvertedExternallyToRlds
Versions:
0.1.0
(default): Initial release.
Download size:
Unknown size
Dataset size:
3.09 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
104 |
- Feature structure:
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
}),
'steps': Dataset({
'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(="zxy") Class].),
'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=(360, 640, 3), dtype=uint8, description=Main camera RGB observation.),
'state': Tensor(shape=(7,), dtype=float32, description=Robot state, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(="zxy") Class].),
}),
'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 | (7,) | float32 | Robot action, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(="zxy") Class]. |
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 | Pour into the mug. | |
steps/observation | FeaturesDict | |||
steps/observation/image | Image | (360, 640, 3) | uint8 | Main camera RGB observation. |
steps/observation/state | Tensor | (7,) | float32 | Robot state, consists of [3x robot EEF position, 3x robot EEF orientation yaw/pitch/roll calculated with scipy Rotation.as_euler(="zxy") Class]. |
steps/reward | Scalar | float32 | Reward if provided, 1 on final step for demos. |
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@inproceedings{vogel_edan_2020,
title = {EDAN - an EMG-Controlled Daily Assistant to Help People with Physical Disabilities},
language = {en},
booktitle = {2020 {IEEE}/{RSJ} {International} {Conference} on {Intelligent} {Robots} and {Systems} ({IROS})},
author = {Vogel, Jörn and Hagengruber, Annette and Iskandar, Maged and Quere, Gabriel and Leipscher, Ulrike and Bustamante, Samuel and Dietrich, Alexander and Hoeppner, Hannes and Leidner, Daniel and Albu-Schäffer, Alin},
year = {2020}
}
@inproceedings{quere_shared_2020,
address = {Paris, France},
title = {Shared {Control} {Templates} for {Assistive} {Robotics} },
language = {en},
booktitle = {2020 {IEEE} {International} {Conference} on {Robotics} and {Automation} ({ICRA})},
author = {Quere, Gabriel and Hagengruber, Annette and Iskandar, Maged and Bustamante, Samuel and Leidner, Daniel and Stulp, Freek and Vogel, Joern},
year = {2020},
pages = {7},
}