stanford_robocook_converted_externally_to_rlds

• Description:

Franka preparing dumplings with various tools

• Homepage: https://hshi74.github.io/robocook/

• Source code: tfds.robotics.rtx.StanfordRobocookConvertedExternallyToRlds

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 124.59 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	2,460

• Feature structure:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'extrinsics_1': Tensor(shape=(4, 4), dtype=float32, description=Camera 1 Extrinsic Matrix.),
        'extrinsics_2': Tensor(shape=(4, 4), dtype=float32, description=Camera 2 Extrinsic Matrix.),
        'extrinsics_3': Tensor(shape=(4, 4), dtype=float32, description=Camera 3 Extrinsic Matrix.),
        'extrinsics_4': Tensor(shape=(4, 4), dtype=float32, description=Camera 4 Extrinsic Matrix.),
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32, description=Robot action, consists of [3x robot end-effector velocities, 3x robot end-effector angular velocities, 1x gripper velocity].),
        '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({
            'depth_1': Tensor(shape=(256, 256), dtype=float32, description=Camera 1 Depth observation.),
            'depth_2': Tensor(shape=(256, 256), dtype=float32, description=Camera 2 Depth observation.),
            'depth_3': Tensor(shape=(256, 256), dtype=float32, description=Camera 3 Depth observation.),
            'depth_4': Tensor(shape=(256, 256), dtype=float32, description=Camera 4 Depth observation.),
            'image_1': Image(shape=(256, 256, 3), dtype=uint8, description=Camera 1 RGB observation.),
            'image_2': Image(shape=(256, 256, 3), dtype=uint8, description=Camera 2 RGB observation.),
            'image_3': Image(shape=(256, 256, 3), dtype=uint8, description=Camera 3 RGB observation.),
            'image_4': Image(shape=(256, 256, 3), dtype=uint8, description=Camera 4 RGB observation.),
            'state': Tensor(shape=(7,), dtype=float32, description=Robot state, consists of [3x robot end-effector position, 3x robot end-effector euler angles, 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/extrinsics_1	Tensor	(4, 4)	float32	Camera 1 Extrinsic Matrix.
episode_metadata/extrinsics_2	Tensor	(4, 4)	float32	Camera 2 Extrinsic Matrix.
episode_metadata/extrinsics_3	Tensor	(4, 4)	float32	Camera 3 Extrinsic Matrix.
episode_metadata/extrinsics_4	Tensor	(4, 4)	float32	Camera 4 Extrinsic Matrix.
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 end-effector velocities, 3x robot end-effector angular velocities, 1x gripper velocity].
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/depth_1	Tensor	(256, 256)	float32	Camera 1 Depth observation.
steps/observation/depth_2	Tensor	(256, 256)	float32	Camera 2 Depth observation.
steps/observation/depth_3	Tensor	(256, 256)	float32	Camera 3 Depth observation.
steps/observation/depth_4	Tensor	(256, 256)	float32	Camera 4 Depth observation.
steps/observation/image_1	Image	(256, 256, 3)	uint8	Camera 1 RGB observation.
steps/observation/image_2	Image	(256, 256, 3)	uint8	Camera 2 RGB observation.
steps/observation/image_3	Image	(256, 256, 3)	uint8	Camera 3 RGB observation.
steps/observation/image_4	Image	(256, 256, 3)	uint8	Camera 4 RGB observation.
steps/observation/state	Tensor	(7,)	float32	Robot state, consists of [3x robot end-effector position, 3x robot end-effector euler angles, 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:

@article{shi2023robocook,
  title={RoboCook: Long-Horizon Elasto-Plastic Object Manipulation with Diverse Tools},
  author={Shi, Haochen and Xu, Huazhe and Clarke, Samuel and Li, Yunzhu and Wu, Jiajun},
  journal={arXiv preprint arXiv:2306.14447},
  year={2023}
}