• Description:

The robot plays with 3 complex scenes: a grill with many cooking objects like toaster, pan, etc. It has to pick, open, place, close. It has to set a table, move plates, cups, utensils. And it has to place dishes in the sink, dishwasher, hand cups etc.

Split Examples
'train' 576
  • Feature structure:
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    'steps': Dataset({
        'action': Tensor(shape=(9,), 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=(128, 128, 3), dtype=uint8),
            'state': Tensor(shape=(8,), 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 (9,) float32 Robot action, consists of [7x delta eef (pos + quat), 1x gripper open/close (binary), 1x terminate episode].
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/image Image (128, 128, 3) uint8 Main camera RGB observation.
steps/observation/state Tensor (8,) float32 Robot state, consists of [7x robot joint angles, 1x gripper position.
steps/reward Scalar float32 Reward if provided, 1 on final step for demos.
  • Citation:
  title={PlayFusion: Skill Acquisition via Diffusion from Language-Annotated Play},
  author={Chen, Lili and Bahl, Shikhar and Pathak, Deepak},