utokyo_saytap_converted_externally_to_rlds

  • Description:

A1 walking, no RGB

Split Examples
'train' 20
  • Feature structure:
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(12,), dtype=float32),
        'discount': Scalar(shape=(), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32),
        'language_instruction': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'desired_pattern': Tensor(shape=(4, 5), dtype=bool),
            'desired_vel': Tensor(shape=(3,), dtype=float32),
            'image': Image(shape=(64, 64, 3), dtype=uint8),
            'prev_act': Tensor(shape=(12,), dtype=float32),
            'proj_grav_vec': Tensor(shape=(3,), dtype=float32),
            'state': Tensor(shape=(30,), dtype=float32),
            'wrist_image': Image(shape=(64, 64, 3), dtype=uint8),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})
  • 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 (12,) float32 Robot action, consists of [12x joint positios].
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/desired_pattern Tensor (4, 5) bool Desired foot contact pattern for the 4 legs, the 4 rows are for the front right, front left, rear right and rear left legs, the pattern length is 5 (=0.1s).
steps/observation/desired_vel Tensor (3,) float32 Desired velocites. The first 2 are linear velocities along and perpendicular to the heading direction, the 3rd is the desired angular velocity about the yaw axis.
steps/observation/image Image (64, 64, 3) uint8 Dummy camera RGB observation.
steps/observation/prev_act Tensor (12,) float32 Actions applied in the previous step.
steps/observation/proj_grav_vec Tensor (3,) float32 The gravity vector [0, 0, -1] in the robot base frame.
steps/observation/state Tensor (30,) float32 Robot state, consists of [3x robot base linear velocity, 3x base angular vel, 12x joint position, 12x joint velocity].
steps/observation/wrist_image Image (64, 64, 3) uint8 Dummy wrist camera RGB observation.
steps/reward Scalar float32 Reward if provided, 1 on final step for demos. 
@article{saytap2023,
  author = {Yujin Tang and Wenhao Yu and Jie Tan and Heiga Zen and Aleksandra Faust and
Tatsuya Harada},
  title  = {SayTap: Language to Quadrupedal Locomotion},
  eprint = {arXiv:2306.07580},
  url    = {https://saytap.github.io},
  note   = "{https://saytap.github.io}",
  year   = {2023}
}