• Description:

UR5 planar pushing tasks

Split Examples
'test' 14
'train' 122
  • Feature structure:
    'steps': Dataset({
        'action': FeaturesDict({
            'gripper_closedness_action': float32,
            'rotation_delta': Tensor(shape=(3,), dtype=float32),
            'terminate_episode': float32,
            'world_vector': Tensor(shape=(3,), dtype=float32),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'image': Image(shape=(240, 320, 3), dtype=uint8),
            'natural_language_embedding': Tensor(shape=(512,), dtype=float32),
            'natural_language_instruction': string,
            'robot_state': Tensor(shape=(2,), dtype=float32),
            'wrist_image': Image(shape=(240, 320, 3), dtype=uint8),
        'reward': Scalar(shape=(), dtype=float32),
  • Feature documentation:
Feature Class Shape Dtype Description
steps Dataset
steps/action FeaturesDict
steps/action/gripper_closedness_action Tensor float32 1 if close gripper, -1 if open gripper, 0 if no change.
steps/action/rotation_delta Tensor (3,) float32 Delta change in roll, pitch, yaw.
steps/action/terminate_episode Tensor float32
steps/action/world_vector Tensor (3,) float32 Delta change in XYZ.
steps/is_first Tensor bool
steps/is_last Tensor bool
steps/is_terminal Tensor bool
steps/observation FeaturesDict
steps/observation/image Image (240, 320, 3) uint8
steps/observation/natural_language_embedding Tensor (512,) float32
steps/observation/natural_language_instruction Tensor string
steps/observation/robot_state Tensor (2,) float32 Robot end effector XY state
steps/observation/wrist_image Image (240, 320, 3) uint8
steps/reward Scalar float32
  • Citation:
    title={Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
    author={Chi, Cheng and Feng, Siyuan and Du, Yilun and Xu, Zhenjia and Cousineau, Eric and Burchfiel, Benjamin and Song, Shuran},
    booktitle={Proceedings of Robotics: Science and Systems (RSS)},