stanford_mask_vit_converted_externally_to_rlds

• বর্ণনা :

Sawyer একটি বিনে বস্তু ঠেলে ও বাছাই

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

• উত্স কোড : tfds.robotics.rtx.StanfordMaskVitConvertedExternallyToRlds

• সংস্করণ :

  • 0.1.0 (ডিফল্ট): প্রাথমিক প্রকাশ।

• ডাউনলোড আকার : Unknown size

• ডেটাসেটের আকার : 76.17 GiB

• স্বয়ংক্রিয় ক্যাশে ( ডকুমেন্টেশন ): না

• বিভাজন :

• বৈশিষ্ট্য গঠন :

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.),
    }),
})

• বৈশিষ্ট্য ডকুমেন্টেশন :

• তত্ত্বাবধান করা কী (দেখুন as_supervised doc ): None

• চিত্র ( tfds.show_examples ): সমর্থিত নয়।

• উদাহরণ ( tfds.as_dataframe ): অনুপস্থিত।

• উদ্ধৃতি :

@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}
}