berkeley_gnm_cory_hall

  • Description:

hallway navigation

Split Examples
'train' 7,331
  • Feature structure:
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=string),
    }),
    'steps': Dataset({
        'action': Tensor(shape=(2,), dtype=float64, description=Robot action, consists of 2x position),
        'action_angle': Tensor(shape=(3,), dtype=float64, description=Robot action, consists of 2x position, 1x yaw),
        'discount': Scalar(shape=(), dtype=float64, description=Discount if provided, default to 1.),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'language_embedding': Tensor(shape=(512,), dtype=float32, description=Kona language embedding. See https://tfhub.dev/google/universal-sentence-encoder-large/5),
        'language_instruction': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'image': Image(shape=(64, 85, 3), dtype=uint8, description=Main camera RGB observation.),
            'position': Tensor(shape=(2,), dtype=float64, description=Robot position),
            'state': Tensor(shape=(3,), dtype=float64, description=Robot state, consists of [2x position, 1x yaw]),
            'yaw': Tensor(shape=(1,), dtype=float64, description=Robot yaw),
        }),
        'reward': Scalar(shape=(), dtype=float64, description=Reward if provided, 1 on final step for demos.),
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
episode_metadata FeaturesDict
episode_metadata/file_path Text string Path to the original data file.
steps Dataset
steps/action Tensor (2,) float64 Robot action, consists of 2x position
steps/action_angle Tensor (3,) float64 Robot action, consists of 2x position, 1x yaw
steps/discount Scalar float64 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 (64, 85, 3) uint8 Main camera RGB observation.
steps/observation/position Tensor (2,) float64 Robot position
steps/observation/state Tensor (3,) float64 Robot state, consists of [2x position, 1x yaw]
steps/observation/yaw Tensor (1,) float64 Robot yaw
steps/reward Scalar float64 Reward if provided, 1 on final step for demos.
  • Citation:
@inproceedings{kahn2018self,
  title={Self-supervised deep reinforcement learning with generalized computation graphs for robot navigation},
  author={Kahn, Gregory and Villaflor, Adam and Ding, Bosen and Abbeel, Pieter and Levine, Sergey},
  booktitle={2018 IEEE international conference on robotics and automation (ICRA)},
  pages={5129--5136},
  year={2018},
  organization={IEEE}
}