- Description:
RoboNet contains over 15 million video frames of robot-object interaction, taken from 113 unique camera viewpoints.
The actions are deltas in position and rotation to the robot end-effector with one additional dimension of the action vector reserved for the gripper joint.
The states are cartesian end-effector control action space with restricted rotation, and a gripper joint
Additional Documentation: Explore on Papers With Code
Homepage: https://www.robonet.wiki/
Source code:
tfds.datasets.robonet.Builder
Versions:
4.0.1
(default): No release notes.
Supervised keys (See
as_supervised
doc):None
Figure (tfds.show_examples): Not supported.
Citation:
@article{dasari2019robonet,
title={RoboNet: Large-Scale Multi-Robot Learning},
author={Dasari, Sudeep and Ebert, Frederik and Tian, Stephen and
Nair, Suraj and Bucher, Bernadette and Schmeckpeper, Karl
and Singh, Siddharth and Levine, Sergey and Finn, Chelsea},
journal={arXiv preprint arXiv:1910.11215},
year={2019}
}
robonet/robonet_sample_64 (default config)
Config description: 64x64 RoboNet Sample.
Download size:
119.80 MiB
Dataset size:
183.04 MiB
Auto-cached (documentation): Only when
shuffle_files=False
(train)Splits:
Split | Examples |
---|---|
'train' |
700 |
- Feature structure:
FeaturesDict({
'actions': Tensor(shape=(None, 5), dtype=float32),
'filename': Text(shape=(), dtype=string),
'states': Tensor(shape=(None, 5), dtype=float32),
'video': Video(Image(shape=(64, 64, 3), dtype=uint8)),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
actions | Tensor | (None, 5) | float32 | |
filename | Text | string | ||
states | Tensor | (None, 5) | float32 | |
video | Video(Image) | (None, 64, 64, 3) | uint8 |
- Examples (tfds.as_dataframe):
robonet/robonet_sample_128
Config description: 128x128 RoboNet Sample.
Download size:
119.80 MiB
Dataset size:
638.98 MiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
700 |
- Feature structure:
FeaturesDict({
'actions': Tensor(shape=(None, 5), dtype=float32),
'filename': Text(shape=(), dtype=string),
'states': Tensor(shape=(None, 5), dtype=float32),
'video': Video(Image(shape=(128, 128, 3), dtype=uint8)),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
actions | Tensor | (None, 5) | float32 | |
filename | Text | string | ||
states | Tensor | (None, 5) | float32 | |
video | Video(Image) | (None, 128, 128, 3) | uint8 |
- Examples (tfds.as_dataframe):
robonet/robonet_64
Config description: 64x64 RoboNet.
Download size:
36.20 GiB
Dataset size:
41.37 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
162,417 |
- Feature structure:
FeaturesDict({
'actions': Tensor(shape=(None, 5), dtype=float32),
'filename': Text(shape=(), dtype=string),
'states': Tensor(shape=(None, 5), dtype=float32),
'video': Video(Image(shape=(64, 64, 3), dtype=uint8)),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
actions | Tensor | (None, 5) | float32 | |
filename | Text | string | ||
states | Tensor | (None, 5) | float32 | |
video | Video(Image) | (None, 64, 64, 3) | uint8 |
- Examples (tfds.as_dataframe):
robonet/robonet_128
Config description: 128x128 RoboNet.
Download size:
36.20 GiB
Dataset size:
144.90 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
162,417 |
- Feature structure:
FeaturesDict({
'actions': Tensor(shape=(None, 5), dtype=float32),
'filename': Text(shape=(), dtype=string),
'states': Tensor(shape=(None, 5), dtype=float32),
'video': Video(Image(shape=(128, 128, 3), dtype=uint8)),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
actions | Tensor | (None, 5) | float32 | |
filename | Text | string | ||
states | Tensor | (None, 5) | float32 | |
video | Video(Image) | (None, 128, 128, 3) | uint8 |
- Examples (tfds.as_dataframe):