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robosuite_panda_pick_place_can

Description:

These datasets have been created with the PickPlaceCan environment of the robosuite robotic arm simulator. The human datasets were recorded by a single operator using the RLDS Creator and a gamepad controller.

The synthetic datasets have been recorded using the EnvLogger library.

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

Episodes consist of 400 steps. In each episode, a tag is added when the task is completed, this tag is stored as part of the custom step metadata.

Note that, due to the EnvLogger dependency, generation of this dataset is currently supported on Linux environments only.

Source code: tfds.rlds.datasets.robosuite_panda_pick_place_can.RobosuitePandaPickPlaceCan
Versions:
- 1.0.0 (default): Initial release.
Auto-cached (documentation): No
Supervised keys (See as_supervised doc): None
Figure (tfds.show_examples): Not supported.
Citation:

@misc{ramos2021rlds,
      title={RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning},
      author={Sabela Ramos and Sertan Girgin and Léonard Hussenot and Damien Vincent and Hanna Yakubovich and Daniel Toyama and Anita Gergely and Piotr Stanczyk and Raphael Marinier and Jeremiah Harmsen and Olivier Pietquin and Nikola Momchev},
      year={2021},
      eprint={2111.02767},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

robosuite_panda_pick_place_can/human_dc29b40a (default config)

Config description: Human generated dataset (50 episodes).
Homepage: https://github.com/google-research/rlds
Download size: 96.67 MiB
Dataset size: 407.24 MiB
Splits:

Split	Examples
`'train'`	50

Feature structure:

FeaturesDict({
    'agent_id': string,
    'episode_id': string,
    'episode_index': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': float64,
        'image': Image(shape=(None, None, 3), dtype=uint8),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'Can_pos': Tensor(shape=(3,), dtype=float64),
            'Can_quat': Tensor(shape=(4,), dtype=float64),
            'Can_to_robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'Can_to_robot0_eef_quat': Tensor(shape=(4,), dtype=float32),
            'object-state': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            '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),
            'robot0_proprio-state': Tensor(shape=(32,), dtype=float64),
        }),
        'reward': float64,
        'tag:placed': bool,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
agent_id	Tensor		string
episode_id	Tensor		string
episode_index	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		float64
steps/image	Image	(None, None, 3)	uint8
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/Can_pos	Tensor	(3,)	float64
steps/observation/Can_quat	Tensor	(4,)	float64
steps/observation/Can_to_robot0_eef_pos	Tensor	(3,)	float64
steps/observation/Can_to_robot0_eef_quat	Tensor	(4,)	float32
steps/observation/object-state	Tensor	(14,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64
steps/observation/robot0_eef_quat	Tensor	(4,)	float64
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64
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
steps/observation/robot0_proprio-state	Tensor	(32,)	float64
steps/reward	Tensor		float64
steps/tag:placed	Tensor		bool

Examples (tfds.as_dataframe):

robosuite_panda_pick_place_can/human_images_dc29b40a

Config description: Human generated dataset, including images with different camera angles in the observation. Note that it may take some time to generate.
Homepage: https://www.tensorflow.org/datasets/catalog/robosuite_panda_pick_place_can
Download size: 10.95 GiB
Dataset size: 7.53 GiB
Splits:

Split	Examples
`'train'`	50

Feature structure:

FeaturesDict({
    'agent_id': string,
    'episode_id': string,
    'episode_index': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float64),
        'discount': float64,
        'image': Image(shape=(None, None, 3), dtype=uint8),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'Can_pos': Tensor(shape=(3,), dtype=float64),
            'Can_quat': Tensor(shape=(4,), dtype=float64),
            'Can_to_robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'Can_to_robot0_eef_quat': Tensor(shape=(4,), dtype=float32),
            'agentview_image': Image(shape=(256, 256, 3), dtype=uint8),
            'birdview_image': Image(shape=(256, 256, 3), dtype=uint8),
            'object-state': Tensor(shape=(14,), dtype=float64),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float64),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float64),
            'robot0_eye_in_hand_image': Image(shape=(256, 256, 3), dtype=uint8),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float64),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float64),
            '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),
            'robot0_proprio-state': Tensor(shape=(32,), dtype=float64),
            'robot0_robotview_image': Image(shape=(256, 256, 3), dtype=uint8),
        }),
        'reward': float64,
        'tag:placed': bool,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
agent_id	Tensor		string
episode_id	Tensor		string
episode_index	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float64
steps/discount	Tensor		float64
steps/image	Image	(None, None, 3)	uint8
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/Can_pos	Tensor	(3,)	float64
steps/observation/Can_quat	Tensor	(4,)	float64
steps/observation/Can_to_robot0_eef_pos	Tensor	(3,)	float64
steps/observation/Can_to_robot0_eef_quat	Tensor	(4,)	float32
steps/observation/agentview_image	Image	(256, 256, 3)	uint8
steps/observation/birdview_image	Image	(256, 256, 3)	uint8
steps/observation/object-state	Tensor	(14,)	float64
steps/observation/robot0_eef_pos	Tensor	(3,)	float64
steps/observation/robot0_eef_quat	Tensor	(4,)	float64
steps/observation/robot0_eye_in_hand_image	Image	(256, 256, 3)	uint8
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float64
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float64
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
steps/observation/robot0_proprio-state	Tensor	(32,)	float64
steps/observation/robot0_robotview_image	Image	(256, 256, 3)	uint8
steps/reward	Tensor		float64
steps/tag:placed	Tensor		bool

Examples (tfds.as_dataframe):

robosuite_panda_pick_place_can/synthetic_stochastic_sac_afe13968

Config description: Synthetic dataset generated by a stochastic agent trained with SAC (200 episodes).
Homepage: https://github.com/google-research/rlds
Download size: 144.44 MiB
Dataset size: 622.86 MiB
Splits:

Split	Examples
`'train'`	200

Feature structure:

FeaturesDict({
    'agent_id': string,
    'episode_id': string,
    'episode_index': int32,
    'steps': Dataset({
        'action': Tensor(shape=(7,), dtype=float32),
        'discount': float64,
        'image': Image(shape=(None, None, 3), dtype=uint8),
        'is_first': bool,
        'is_last': bool,
        'is_terminal': bool,
        'observation': FeaturesDict({
            'Can_pos': Tensor(shape=(3,), dtype=float32),
            'Can_quat': Tensor(shape=(4,), dtype=float32),
            'Can_to_robot0_eef_pos': Tensor(shape=(3,), dtype=float32),
            'Can_to_robot0_eef_quat': Tensor(shape=(4,), dtype=float32),
            'object-state': Tensor(shape=(14,), dtype=float32),
            'robot0_eef_pos': Tensor(shape=(3,), dtype=float32),
            'robot0_eef_quat': Tensor(shape=(4,), dtype=float32),
            'robot0_gripper_qpos': Tensor(shape=(2,), dtype=float32),
            'robot0_gripper_qvel': Tensor(shape=(2,), dtype=float32),
            'robot0_joint_pos_cos': Tensor(shape=(7,), dtype=float32),
            'robot0_joint_pos_sin': Tensor(shape=(7,), dtype=float32),
            'robot0_joint_vel': Tensor(shape=(7,), dtype=float32),
            'robot0_proprio-state': Tensor(shape=(32,), dtype=float32),
        }),
        'reward': float64,
        'tag:placed': bool,
    }),
})

Feature documentation:

Feature	Class	Shape	Dtype
	FeaturesDict
agent_id	Tensor		string
episode_id	Tensor		string
episode_index	Tensor		int32
steps	Dataset
steps/action	Tensor	(7,)	float32
steps/discount	Tensor		float64
steps/image	Image	(None, None, 3)	uint8
steps/is_first	Tensor		bool
steps/is_last	Tensor		bool
steps/is_terminal	Tensor		bool
steps/observation	FeaturesDict
steps/observation/Can_pos	Tensor	(3,)	float32
steps/observation/Can_quat	Tensor	(4,)	float32
steps/observation/Can_to_robot0_eef_pos	Tensor	(3,)	float32
steps/observation/Can_to_robot0_eef_quat	Tensor	(4,)	float32
steps/observation/object-state	Tensor	(14,)	float32
steps/observation/robot0_eef_pos	Tensor	(3,)	float32
steps/observation/robot0_eef_quat	Tensor	(4,)	float32
steps/observation/robot0_gripper_qpos	Tensor	(2,)	float32
steps/observation/robot0_gripper_qvel	Tensor	(2,)	float32
steps/observation/robot0_joint_pos_cos	Tensor	(7,)	float32
steps/observation/robot0_joint_pos_sin	Tensor	(7,)	float32
steps/observation/robot0_joint_vel	Tensor	(7,)	float32
steps/observation/robot0_proprio-state	Tensor	(32,)	float32
steps/reward	Tensor		float64
steps/tag:placed	Tensor		bool

Examples (tfds.as_dataframe):