- Description:
Franka opening ovens -- point cloud + proprio only
Homepage: https://ieeexplore.ieee.org/iel7/10160211/10160212/10160747.pdf
Source code:
tfds.robotics.rtx.EthAgentAffordances
Versions:
0.1.0
(default): Initial release.
Download size:
Unknown size
Dataset size:
17.27 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'train' |
118 |
- Feature structure:
FeaturesDict({
'episode_metadata': FeaturesDict({
'file_path': Text(shape=(), dtype=string),
'input_point_cloud': Tensor(shape=(10000, 3), dtype=float16, description=Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3).),
}),
'steps': Dataset({
'action': Tensor(shape=(6,), dtype=float32, description=Robot action, consists of [end-effector velocity (v_x,v_y,v_z,omega_x,omega_y,omega_z) in world frame),
'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=(64, 64, 3), dtype=uint8, description=Main camera RGB observation. Not available for this dataset, will be set to np.zeros.),
'input_point_cloud': Tensor(shape=(10000, 3), dtype=float16, description=Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3).),
'state': Tensor(shape=(8,), dtype=float32, description=State, consists of [end-effector pose (x,y,z,yaw,pitch,roll) in world frame, 1x gripper open/close, 1x door opening angle].),
}),
'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. | |
episode_metadata/input_point_cloud | Tensor | (10000, 3) | float16 | Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3). |
steps | Dataset | |||
steps/action | Tensor | (6,) | float32 | Robot action, consists of [end-effector velocity (v_x,v_y,v_z,omega_x,omega_y,omega_z) in world frame |
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 | (64, 64, 3) | uint8 | Main camera RGB observation. Not available for this dataset, will be set to np.zeros. |
steps/observation/input_point_cloud | Tensor | (10000, 3) | float16 | Point cloud (geometry only) of the object at the beginning of the episode (world frame) as a numpy array (10000,3). |
steps/observation/state | Tensor | (8,) | float32 | State, consists of [end-effector pose (x,y,z,yaw,pitch,roll) in world frame, 1x gripper open/close, 1x door opening angle]. |
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{schiavi2023learning,
title={Learning agent-aware affordances for closed-loop interaction with articulated objects},
author={Schiavi, Giulio and Wulkop, Paula and Rizzi, Giuseppe and Ott, Lionel and Siegwart, Roland and Chung, Jen Jen},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
pages={5916--5922},
year={2023},
organization={IEEE}
}