- Description:
Hello stretch robot kitchen interactions
Homepage: https://robo-affordances.github.io/
Source code:
tfds.robotics.rtx.CmuStretch
Versions:
0.1.0
(default): Initial release.
Download size:
Unknown size
Dataset size:
728.06 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
135 |
- Feature structure:
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
}),
'steps': Dataset({
'action': Tensor(shape=(8,), dtype=float32, description=Robot action, consists of [3x ee pos, 3x ee rot 1x gripper binary action, 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=(128, 128, 3), dtype=uint8, description=Main camera RGB observation.),
'state': Tensor(shape=(4,), dtype=float32, description=Robot state, consists of [3x robot joint angles/ee pos, 1x gripper position].),
}),
'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 | (8,) | float32 | Robot action, consists of [3x ee pos, 3x ee rot 1x gripper binary action, 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 | (128, 128, 3) | uint8 | Main camera RGB observation. |
steps/observation/state | Tensor | (4,) | float32 | Robot state, consists of [3x robot joint angles/ee pos, 1x gripper position]. |
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{bahl2023affordances,
title={Affordances from Human Videos as a Versatile Representation for Robotics},
author={Bahl, Shikhar and Mendonca, Russell and Chen, Lili and Jain, Unnat and Pathak, Deepak},
booktitle={CVPR},
year={2023}
}
@article{mendonca2023structured,
title={Structured World Models from Human Videos},
author={Mendonca, Russell and Bahl, Shikhar and Pathak, Deepak},
journal={CoRL},
year={2023}
}