Split Examples
'train' 24
  • Feature structure:
    'episode_metadata': FeaturesDict({
        'file_path': string,
    'steps': Dataset({
        'action': string,
        'discount': Scalar(shape=(), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32),
        'language_instruction': string,
        'observation': FeaturesDict({
            'image': Image(shape=(360, 640, 3), dtype=uint8),
            'object': string,
            'receptacles': Sequence(string),
        'reward': Scalar(shape=(), dtype=float32),
  • Feature documentation:
Feature Class Shape Dtype Description
episode_metadata FeaturesDict
episode_metadata/file_path Tensor string
steps Dataset
steps/action Tensor string
steps/discount Scalar float32
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/language_embedding Tensor (512,) float32
steps/language_instruction Tensor string
steps/observation FeaturesDict
steps/observation/image Image (360, 640, 3) uint8
steps/observation/object Tensor string
steps/observation/receptacles Sequence(Tensor) (None,) string
steps/reward Scalar float32
@article{wu2023tidybot,title = {TidyBot: Personalized Robot Assistance with Large Language Models},author = {Wu, Jimmy and Antonova, Rika and Kan, Adam and Lepert, Marion and Zeng, Andy and Song, Shuran and Bohg, Jeannette and Rusinkiewicz, Szymon and Funkhouser, Thomas},journal = {Autonomous Robots},year = {2023} }