conq_hose_manipulation

  • Description:

Mobile manipulation dataset

Split Examples
'train' 113
'val' 26
  • Feature structure:
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': string,
    }),
    'steps': Dataset({
        'action': Tensor(shape=(7,), 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': string,
        'observation': FeaturesDict({
            'frontleft_fisheye_image': Image(shape=(726, 604, 3), dtype=uint8),
            'frontright_fisheye_image': Image(shape=(726, 604, 3), dtype=uint8),
            'hand_color_image': Image(shape=(480, 640, 3), dtype=uint8),
            'state': Tensor(shape=(66,), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
episode_metadata FeaturesDict
episode_metadata/file_path Tensor string
steps Dataset
steps/action Tensor (7,) float32
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/frontleft_fisheye_image Image (726, 604, 3) uint8
steps/observation/frontright_fisheye_image Image (726, 604, 3) uint8
steps/observation/hand_color_image Image (480, 640, 3) uint8
steps/observation/state Tensor (66,) float32
steps/reward Scalar float32
  • Citation:
@misc{ConqHoseManipData,
author={Peter Mitrano and Dmitry Berenson},
title={Conq Hose Manipulation Dataset, v1.15.0},
year={2024},
howpublished={https://sites.google.com/view/conq-hose-manipulation-dataset}
}