stanford_mask_vit_converted_externally_to_rlds

  • Description:

Sawyer pushing and picking objects in a bin

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.
@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}
}