maniskill_dataset_converted_externally_to_rlds

  • Description:

Simulated Franka performing various manipulation tasks

Split Examples
'train' 30,213
  • Feature structure:
FeaturesDict({
    'episode_metadata': FeaturesDict({
        'episode_id': Text(shape=(), dtype=string),
        '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({
            'base_pose': Tensor(shape=(7,), dtype=float32),
            'depth': Image(shape=(256, 256, 1), dtype=uint16),
            'image': Image(shape=(256, 256, 3), dtype=uint8),
            'main_camera_cam2world_gl': Tensor(shape=(4, 4), dtype=float32),
            'main_camera_extrinsic_cv': Tensor(shape=(4, 4), dtype=float32),
            'main_camera_intrinsic_cv': Tensor(shape=(3, 3), dtype=float32),
            'state': Tensor(shape=(18,), dtype=float32),
            'target_object_or_part_final_pose': Tensor(shape=(7,), dtype=float32),
            'target_object_or_part_final_pose_valid': Tensor(shape=(7,), dtype=uint8),
            'target_object_or_part_initial_pose': Tensor(shape=(7,), dtype=float32),
            'target_object_or_part_initial_pose_valid': Tensor(shape=(7,), dtype=uint8),
            'tcp_pose': Tensor(shape=(7,), dtype=float32),
            'wrist_camera_cam2world_gl': Tensor(shape=(4, 4), dtype=float32),
            'wrist_camera_extrinsic_cv': Tensor(shape=(4, 4), dtype=float32),
            'wrist_camera_intrinsic_cv': Tensor(shape=(3, 3), dtype=float32),
            'wrist_depth': Image(shape=(256, 256, 1), dtype=uint16),
            'wrist_image': Image(shape=(256, 256, 3), dtype=uint8),
        }),
        'reward': Scalar(shape=(), dtype=float32),
    }),
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
episode_metadata FeaturesDict
episode_metadata/episode_id Text string Episode ID.
episode_metadata/file_path Text string Path to the original data file.
steps Dataset
steps/action Tensor (7,) float32 Robot action, consists of [3x end effector delta target position, 3x end effector delta target orientation in axis-angle format, 1x gripper target position (mimic for two fingers)]. For delta target position, an action of -1 maps to a robot movement of -0.1m, and action of 1 maps to a movement of 0.1m. For delta target orientation, its encoded angle is mapped to a range of [-0.1rad, 0.1rad] for robot execution. For example, an action of [1, 0, 0] means rotating along the x-axis by 0.1 rad. For gripper target position, an action of -1 means close, and an action of 1 means open.
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 https://tfhub.dev/google/universal-sentence-encoder-large/5
steps/language_instruction Text string Language Instruction.
steps/observation FeaturesDict
steps/observation/base_pose Tensor (7,) float32 Robot base pose in the world frame, consists of [x, y, z, qw, qx, qy, qz]. The first three dimensions represent xyz positions in meters. The last four dimensions are the quaternion representation of rotation.
steps/observation/depth Image (256, 256, 1) uint16 Main camera Depth observation. Divide the depth value by 2**10 to get the depth in meters.
steps/observation/image Image (256, 256, 3) uint8 Main camera RGB observation.
steps/observation/main_camera_cam2world_gl Tensor (4, 4) float32 Transformation from the main camera frame to the world frame in OpenGL/Blender convention.
steps/observation/main_camera_extrinsic_cv Tensor (4, 4) float32 Main camera extrinsic matrix in OpenCV convention.
steps/observation/main_camera_intrinsic_cv Tensor (3, 3) float32 Main camera intrinsic matrix in OpenCV convention.
steps/observation/state Tensor (18,) float32 Robot state, consists of [7x robot joint angles, 2x gripper position, 7x robot joint angle velocity, 2x gripper velocity]. Angle in radians, position in meters.
steps/observation/target_object_or_part_final_pose Tensor (7,) float32 The final pose towards which the target object or object part needs be manipulated, consists of [x, y, z, qw, qx, qy, qz]. The pose is represented in the world frame. An episode is considered successful if the target object or object part is manipulated to this pose.
steps/observation/target_object_or_part_final_pose_valid Tensor (7,) uint8 Whether each dimension of target_object_or_part_final_pose is valid in an environment. 1 = valid; 0 = invalid (in which case one should ignore the corresponding dimensions in target_object_or_part_final_pose). "Invalid" means that there is no success check on the final pose of target object or object part in the corresponding dimensions.
steps/observation/target_object_or_part_initial_pose Tensor (7,) float32 The initial pose of the target object or object part to be manipulated, consists of [x, y, z, qw, qx, qy, qz]. The pose is represented in the world frame. This variable is used to specify the target object or object part when multiple objects or object parts are present in an environment
steps/observation/target_object_or_part_initial_pose_valid Tensor (7,) uint8 Whether each dimension of target_object_or_part_initial_pose is valid in an environment. 1 = valid; 0 = invalid (in which case one should ignore the corresponding dimensions in target_object_or_part_initial_pose).
steps/observation/tcp_pose Tensor (7,) float32 Robot tool-center-point pose in the world frame, consists of [x, y, z, qw, qx, qy, qz]. Tool-center-point is the center between the two gripper fingers.
steps/observation/wrist_camera_cam2world_gl Tensor (4, 4) float32 Transformation from the wrist camera frame to the world frame in OpenGL/Blender convention.
steps/observation/wrist_camera_extrinsic_cv Tensor (4, 4) float32 Wrist camera extrinsic matrix in OpenCV convention.
steps/observation/wrist_camera_intrinsic_cv Tensor (3, 3) float32 Wrist camera intrinsic matrix in OpenCV convention.
steps/observation/wrist_depth Image (256, 256, 1) uint16 Wrist camera Depth observation. Divide the depth value by 2**10 to get the depth in meters.
steps/observation/wrist_image Image (256, 256, 3) uint8 Wrist camera RGB observation.
steps/reward Scalar float32 Reward if provided, 1 on final step for demos.
@inproceedings{gu2023maniskill2,
  title={ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills},
  author={Gu, Jiayuan and Xiang, Fanbo and Li, Xuanlin and Ling, Zhan and Liu, Xiqiang and Mu, Tongzhou and Tang, Yihe and Tao, Stone and Wei, Xinyue and Yao, Yunchao and Yuan, Xiaodi and Xie, Pengwei and Huang, Zhiao and Chen, Rui and Su, Hao},
  booktitle={International Conference on Learning Representations},
  year={2023}
}