fmb

• Description:

Our dataset consists of objects in diverse appearance and geometry. It requires multi-stage and multi-modal fine motor skills to successfully assemble the pegs onto a unfixed board in a randomized scene. We collected a total of 22,550 trajectories across two different tasks on a Franka Panda arm. We record the trajectories from 2 global views and 2 wrist views. Each view contains both RGB and depth map.

• Homepage: https://functional-manipulation-benchmark.github.io/

• Source code: tfds.robotics.rtx.Fmb

• Versions:

  • 0.1.0 (default): Initial release.

• Download size: Unknown size

• Dataset size: 356.63 GiB

• Auto-cached (documentation): No

• Splits:

• Feature structure:

FeaturesDict({
    'episode_metadata': FeaturesDict({
        'episode_language_embedding': Tensor(shape=(512,), dtype=float32),
        'episode_language_instruction': string,
        'episode_task': string,
        '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({
            'color_id': Scalar(shape=(), dtype=uint8),
            'eef_force': Tensor(shape=(3,), dtype=float32),
            'eef_pose': Tensor(shape=(7,), dtype=float32),
            'eef_torque': Tensor(shape=(3,), dtype=float32),
            'eef_vel': Tensor(shape=(6,), dtype=float32),
            'image_side_1': Image(shape=(256, 256, 3), dtype=uint8),
            'image_side_1_depth': Tensor(shape=(256, 256), dtype=float32),
            'image_side_2': Image(shape=(256, 256, 3), dtype=uint8),
            'image_side_2_depth': Tensor(shape=(256, 256), dtype=float32),
            'image_wrist_1': Image(shape=(256, 256, 3), dtype=uint8),
            'image_wrist_1_depth': Tensor(shape=(256, 256), dtype=float32),
            'image_wrist_2': Image(shape=(256, 256, 3), dtype=uint8),
            'image_wrist_2_depth': Tensor(shape=(256, 256), dtype=float32),
            'joint_pos': Tensor(shape=(7,), dtype=float32),
            'joint_vel': Tensor(shape=(7,), dtype=float32),
            'length': string,
            'object_id': Scalar(shape=(), dtype=uint8),
            'primitive': string,
            'shape_id': Scalar(shape=(), dtype=uint8),
            'size': string,
            'state_gripper_pose': Scalar(shape=(), dtype=float32),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})

• Feature documentation:

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Examples (tfds.as_dataframe):

• Citation:

https://doi.org/10.48550/arXiv.2401.08553