robomimic_mg

• Description:

The Robomimic machine generated datasets were collected using a Soft Actor Critic agent trained with a dense reward. Each dataset consists of the agent's replay buffer.

Each task has two versions: one with low dimensional observations (low_dim), and one with images (image).

The datasets follow the RLDS format to represent steps and episodes.

• Homepage: https://arise-initiative.github.io/robomimic-web/

• Source code: tfds.datasets.robomimic_mg.Builder

• Versions:

  • 1.0.0 (default): Initial release.

• Supervised keys (See as_supervised doc): None

• Figure (tfds.show_examples): Not supported.

• Citation:

@inproceedings{robomimic2021,
  title={What Matters in Learning from Offline Human Demonstrations for Robot Manipulation},
  author={Ajay Mandlekar and Danfei Xu and Josiah Wong and Soroush Nasiriany
          and Chen Wang and Rohun Kulkarni and Li Fei-Fei and Silvio Savarese
          and Yuke Zhu and Roberto Mart\'{i}n-Mart\'{i}n},
  booktitle={Conference on Robot Learning},
  year={2021}
}

robomimic_mg/lift_mg_image (default config)

• Download size: 18.04 GiB

• Dataset size: 2.73 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	1,500

• Feature structure:

FeaturesDict({
    'episode_id': string,
    'horizon': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64, description=7DOF joint velocities),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
episode_id	Tensor		string
horizon	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		int32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/agentview_image	Image	(84, 84, 3)	uint8
steps/observation/object	Tensor	(10,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64	End-effector position
steps/observation/robot0_eef_quat	Tensor	(4,)	float64	End-effector orientation
steps/observation/robot0_eef_vel_ang	Tensor	(3,)	float64	End-effector angular velocity
steps/observation/robot0_eef_vel_lin	Tensor	(3,)	float64	End-effector cartesian velocity
steps/observation/robot0_eye_in_hand_image	Image	(84, 84, 3)	uint8
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64	Gripper position
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64	Gripper velocity
steps/observation/robot0_joint_pos	Tensor	(7,)	float64	7DOF joint positions
steps/observation/robot0_joint_pos_cos	Tensor	(7,)	float64
steps/observation/robot0_joint_pos_sin	Tensor	(7,)	float64
steps/observation/robot0_joint_vel	Tensor	(7,)	float64	7DOF joint velocities
steps/reward	Tensor		float64
steps/states	Tensor	(32,)	float64

• Examples (tfds.as_dataframe):

robomimic_mg/lift_mg_low_dim

• Download size: 302.25 MiB

• Dataset size: 195.10 MiB

• Auto-cached (documentation): Only when shuffle_files=False (train)

• Splits:

Split	Examples
'train'	1,500

• Feature structure:

FeaturesDict({
    'episode_id': string,
    'horizon': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(10,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64, description=7DOF joint velocities),
        }),
        'reward': float64,
        'states': Tensor(shape=(32,), dtype=float64),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
episode_id	Tensor		string
horizon	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		int32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/object	Tensor	(10,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64	End-effector position
steps/observation/robot0_eef_quat	Tensor	(4,)	float64	End-effector orientation
steps/observation/robot0_eef_vel_ang	Tensor	(3,)	float64	End-effector angular velocity
steps/observation/robot0_eef_vel_lin	Tensor	(3,)	float64	End-effector cartesian velocity
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64	Gripper position
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64	Gripper velocity
steps/observation/robot0_joint_pos	Tensor	(7,)	float64	7DOF joint positions
steps/observation/robot0_joint_pos_cos	Tensor	(7,)	float64
steps/observation/robot0_joint_pos_sin	Tensor	(7,)	float64
steps/observation/robot0_joint_vel	Tensor	(7,)	float64	7DOF joint velocities
steps/reward	Tensor		float64
steps/states	Tensor	(32,)	float64

• Examples (tfds.as_dataframe):

robomimic_mg/can_mg_image

• Download size: 47.14 GiB

• Dataset size: 11.15 GiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	3,900

• Feature structure:

FeaturesDict({
    'episode_id': string,
    'horizon': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'agentview_image': Image(shape=(84, 84, 3), dtype=uint8),
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_eye_in_hand_image': Image(shape=(84, 84, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64, description=7DOF joint velocities),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
episode_id	Tensor		string
horizon	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		int32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/agentview_image	Image	(84, 84, 3)	uint8
steps/observation/object	Tensor	(14,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64	End-effector position
steps/observation/robot0_eef_quat	Tensor	(4,)	float64	End-effector orientation
steps/observation/robot0_eef_vel_ang	Tensor	(3,)	float64	End-effector angular velocity
steps/observation/robot0_eef_vel_lin	Tensor	(3,)	float64	End-effector cartesian velocity
steps/observation/robot0_eye_in_hand_image	Image	(84, 84, 3)	uint8
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64	Gripper position
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64	Gripper velocity
steps/observation/robot0_joint_pos	Tensor	(7,)	float64	7DOF joint positions
steps/observation/robot0_joint_pos_cos	Tensor	(7,)	float64
steps/observation/robot0_joint_pos_sin	Tensor	(7,)	float64
steps/observation/robot0_joint_vel	Tensor	(7,)	float64	7DOF joint velocities
steps/reward	Tensor		float64
steps/states	Tensor	(71,)	float64

• Examples (tfds.as_dataframe):

robomimic_mg/can_mg_low_dim

• Download size: 1.01 GiB

• Dataset size: 697.71 MiB

• Auto-cached (documentation): No

• Splits:

Split	Examples
'train'	3,900

• Feature structure:

FeaturesDict({
    'episode_id': string,
    'horizon': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': int32,
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'object': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64, description=End-effector position),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64, description=End-effector orientation),
            'robot0_eef_vel_ang': Tensor(shape=(3,), dtype=float64, description=End-effector angular velocity),
            'robot0_eef_vel_lin': Tensor(shape=(3,), dtype=float64, description=End-effector cartesian velocity),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64, description=Gripper position),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64, description=Gripper velocity),
            'robot0_joint_pos': Tensor(shape=(7,), dtype=float64, description=7DOF joint positions),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float64),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float64, description=7DOF joint velocities),
        }),
        'reward': float64,
        'states': Tensor(shape=(71,), dtype=float64),
    }),
})

• Feature documentation:

Feature	Class	Shape	Dtype	Description
	FeaturesDict
episode_id	Tensor		string
horizon	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		int32
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/object	Tensor	(14,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64	End-effector position
steps/observation/robot0_eef_quat	Tensor	(4,)	float64	End-effector orientation
steps/observation/robot0_eef_vel_ang	Tensor	(3,)	float64	End-effector angular velocity
steps/observation/robot0_eef_vel_lin	Tensor	(3,)	float64	End-effector cartesian velocity
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64	Gripper position
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64	Gripper velocity
steps/observation/robot0_joint_pos	Tensor	(7,)	float64	7DOF joint positions
steps/observation/robot0_joint_pos_cos	Tensor	(7,)	float64
steps/observation/robot0_joint_pos_sin	Tensor	(7,)	float64
steps/observation/robot0_joint_vel	Tensor	(7,)	float64	7DOF joint velocities
steps/reward	Tensor		float64
steps/states	Tensor	(71,)	float64

• Examples (tfds.as_dataframe):