stanford_mask_vit_converted_externally_to_rlds

• Description:

Sawyer pushing and picking objects in a bin

• Homepage: https://arxiv.org/abs/2206.11894

• Source code: tfds.robotics.rtx.StanfordMaskVitConvertedExternallyToRlds

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 76.17 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	9,109
'val'	91

• Feature structure:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(5,), dtype=float32, description=Robot action, consists of [3x change in end effector position, 1x gripper yaw, 1x open/close gripper (-1 means to open the gripper, 1 means close)].),
        '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({
            'end_effector_pose': Tensor(shape=(5,), dtype=float32, description=Robot end effector pose, consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position]. This is the state used in the MaskViT paper.),
            'finger_sensors': Tensor(shape=(1,), dtype=float32, description=1x Sawyer gripper finger sensors.),
            'high_bound': Tensor(shape=(5,), dtype=float32, description=High bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].),
            'image': Image(shape=(480, 480, 3), dtype=uint8, description=Main camera RGB observation.),
            'low_bound': Tensor(shape=(5,), dtype=float32, description=Low bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].),
            'state': Tensor(shape=(15,), dtype=float32, description=Robot state, consists of [7x robot joint angles, 7x robot joint velocities,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	(5,)	float32	Robot action, consists of [3x change in end effector position, 1x gripper yaw, 1x open/close gripper (-1 means to open the gripper, 1 means close)].
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/end_effector_pose	Tensor	(5,)	float32	Robot end effector pose, consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position]. This is the state used in the MaskViT paper.
steps/observation/finger_sensors	Tensor	(1,)	float32	1x Sawyer gripper finger sensors.
steps/observation/high_bound	Tensor	(5,)	float32	High bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].
steps/observation/image	Image	(480, 480, 3)	uint8	Main camera RGB observation.
steps/observation/low_bound	Tensor	(5,)	float32	Low bound for end effector pose normalization. Consists of [3x Cartesian position, 1x gripper yaw, 1x gripper position].
steps/observation/state	Tensor	(15,)	float32	Robot state, consists of [7x robot joint angles, 7x robot joint velocities,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{gupta2022maskvit,
  title={MaskViT: Masked Visual Pre-Training for Video Prediction},
  author={Agrim Gupta and Stephen Tian and Yunzhi Zhang and Jiajun Wu and Roberto Martín-Martín and Li Fei-Fei},
  booktitle={International Conference on Learning Representations},
  year={2022}
}