• Description:

Franka exploring toy kitchens

Split Examples
'train' 199
  • Feature structure:
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    'steps': Dataset({
        'action': Tensor(shape=(8,), 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({
            'highres_image': Image(shape=(480, 640, 3), dtype=uint8),
            'image': Image(shape=(64, 64, 3), dtype=uint8),
        'reward': Scalar(shape=(), dtype=float32),
        'structured_action': Tensor(shape=(8,), dtype=float32),
  • Feature documentation:
Feature Class Shape Dtype Description
episode_metadata FeaturesDict
episode_metadata/file_path Text string Path to the original data file.
steps Dataset
steps/action Tensor (8,) float32 Robot action, consists of [end effector position3x, end effector orientation3x, gripper action1x, episode termination1x].
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
steps/language_instruction Text string Language Instruction.
steps/observation FeaturesDict
steps/observation/highres_image Image (480, 640, 3) uint8 High resolution main camera observation
steps/observation/image Image (64, 64, 3) uint8 Main camera RGB observation.
steps/reward Scalar float32 Reward if provided, 1 on final step for demos.
steps/structured_action Tensor (8,) float32 Structured action, consisting of hybrid affordance and end-effector control, described in Structured World Models from Human Videos.
  • Citation:
              title={Structured World Models from Human Videos},
              author={Mendonca, Russell  and Bahl, Shikhar and Pathak, Deepak},