• Description:

motion planning trajectory of pick place tasks

Split Examples
'train' 50
  • Feature structure:
    'episode_metadata': FeaturesDict({
        'file_path': Text(shape=(), dtype=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': Text(shape=(), dtype=string),
        'observation': FeaturesDict({
            'image': Image(shape=(120, 120, 3), dtype=uint8),
            'state': Tensor(shape=(13,), 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 (7,) float32 Robot action, consists of [3x endeffector position, 3x euler angles,1x gripper 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
steps/language_instruction Text string Language Instruction.
steps/observation FeaturesDict
steps/observation/image Image (120, 120, 3) uint8 Main camera RGB observation.
steps/observation/state Tensor (13,) float32 Robot state, consists of [3x endeffector position, 3x euler angles,6x robot joint angles, 1x gripper position].
steps/reward Scalar float32 Reward if provided, 1 on final step for demos.
  author  = {Takayuki Osa},
  journal = {The International Journal of Robotics Research},
  title   = {Motion Planning by Learning the Solution Manifold in Trajectory Optimization},
  year    = {2022},
  number  = {3},
  pages   = {291--311},
  volume  = {41},