• Description:

xArm interacting with different toy kitchens

Split Examples
'train' 150
  • 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({
            'image': Image(shape=(480, 640, 3), dtype=uint8),
            'state': Tensor(shape=(21,), dtype=float32),
        'reward': Scalar(shape=(), 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 8-dimensional action, consisting of end-effector position and orientation, gripper open/close and a episode termination action.
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/image Image (480, 640, 3) uint8 Main camera RGB observation.
steps/observation/state Tensor (21,) float32 21-dimensional joint states, consists of robot joint angles, joint velocity and joint torque.
steps/reward Scalar float32 Reward if provided, 1 on final step for demos.
  author = {Ge Yan, Kris Wu, and Xiaolong Wang},
  title = { {ucsd kitchens Dataset} },
  year = {2023},
  month = {August}