Dataset Card for CareManip (HDF5 Format)

CareManip is a real-world leader-follower robot teleoperation dataset for care-oriented tabletop manipulation. The release contains 15 task categories and 1,500 HDF5 episodes. Each HDF5 file records one complete demonstration trajectory and preserves the original action and robot-state arrays for reproducible use in robot learning research.

Dataset release: v1.0
Dataset DOI: To be generated after the final public release
Associated paper: To be added
License: CC BY 4.0

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Dataset Description

CareManip was collected to support research on robot imitation learning, embodied AI, assistive robotics, and multimodal tabletop manipulation. The task suite includes care-oriented object pick-and-place and push operations designed around everyday object handling.

The control data follow a leader-follower configuration:

• The action vector has 14 dimensions. It represents the left and right leader wrist poses - position and orientation - together with two leader gripper commands.
• The robot state vector (qpos) has 16 dimensions. It represents 14 follower-arm joint positions - seven joints per arm - and two follower claw positions.

This separation makes it possible to study mappings from leader-space teleoperation commands to follower-robot joint states, as well as sequence-policy learning from visual, state, and action observations.

Release Statistics

Item	Value
Task categories	15
Demonstration episodes	1,500
Episodes per task	100
Total frames	414,178
Mean frames per episode	276.12
Minimum episode length	74 frames
Maximum episode length	1,321 frames
Raw trajectory format	HDF5 (.hdf5)
Action dimensionality	14
Follower state dimensionality (qpos)	16

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Task Categories

The release is organized as one directory per task under data/. Each task directory contains the 100 HDF5 demonstrations belonging to that task.

Task directory	Manipulation type	Task definition
pick_block	Pick and place	Pick up the block and place it into the storage box.
pick_bin_bag	Pick and place	Pick up the bin bag and place it into the storage box.
pick_brown_bottle	Pick and place	Pick up the brown bottle and place it into the storage box.
pick_mask	Pick and place	Pick up the mask and place it into the storage box.
pick_medicine	Pick and place	Pick up the medicine item and place it into the storage box.
pick_medicine_bottle	Pick and place	Pick up the medicine bottle and place it into the storage box.
pick_one_medicine_bottle	Pick and place	Pick up one medicine bottle and place it into the storage box.
pick_toy	Pick and place	Pick up the toy and place it into the storage box.
pick_vitamin_bottle	Pick and place	Pick up the vitamin bottle and place it into the storage box.
pick_white_bottle	Pick and place	Pick up the white bottle and place it into the storage box.
push_cotton_swab_holder	Push	Push the cotton swab holder to the designated position.
push_cup	Push	Push the cup to the designated position.
push_glasses_box	Push	Push the glasses box to the designated position.
push_tissues_box	Push	Push the tissues box to the designated position.
push_towel	Push	Push the towel to the designated position.

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File Structure

Each .hdf5 file represents one complete trajectory, also referred to as an episode.

.
├── README.md
├── LICENSE
├── CITATION.cff
└── data/
    ├── pick_block/
    │   ├── pick_block0000.hdf5
    │   ├── pick_block0001.hdf5
    │   ├── ...
    │   └── pick_block0099.hdf5
    ├── pick_bin_bag/
    │   ├── pick_bin_bag0000.hdf5
    │   ├── ...
    │   └── pick_bin_bag0099.hdf5
    ├── pick_brown_bottle/
    ├── pick_mask/
    ├── pick_medicine/
    ├── pick_medicine_bottle/
    ├── pick_one_medicine_bottle/
    ├── pick_toy/
    ├── pick_vitamin_bottle/
    ├── pick_white_bottle/
    ├── push_cotton_swab_holder/
    ├── push_cup/
    ├── push_glasses_box/
    ├── push_tissues_box/
    └── push_towel/

File naming convention:

<task_name><zero-padded_episode_index>.hdf5

For example:

pick_block0004.hdf5

denotes the fifth recorded episode of the pick_block task.

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HDF5 Internal Structure

Each HDF5 episode contains an action sequence and a follower-robot joint-state sequence. Let T denote the number of synchronized time steps in one episode.

Key	Shape	Description
/action	(T, 14)	Leader-space teleoperation action vector.
/observations/qpos	(T, 16)	Follower-robot joint position state vector.
actionnames	14 names	Ordered names of the action-vector dimensions.
qpos names	16 names	Ordered names of the follower-state-vector dimensions.

The exact HDF5 group path used for dimension-name metadata may differ between recording versions. The semantic order below is the authoritative interpretation of the 14-dimensional action and 16-dimensional qpos vectors.

Action Vector: /action

The action vector contains two six-degree-of-freedom leader-wrist pose representations and two leader-gripper values.

Index	Dimension name	Description
0	leader_left_wrist/leader_left_wrist_x	Left leader-wrist x-position command.
1	leader_left_wrist/leader_left_wrist_y	Left leader-wrist y-position command.
2	leader_left_wrist/leader_left_wrist_z	Left leader-wrist z-position command.
3	leader_left_wrist/leader_left_wrist_roll	Left leader-wrist roll command.
4	leader_left_wrist/leader_left_wrist_pitch	Left leader-wrist pitch command.
5	leader_left_wrist/leader_left_wrist_yaw	Left leader-wrist yaw command.
6	leader_right_wrist/leader_right_wrist_x	Right leader-wrist x-position command.
7	leader_right_wrist/leader_right_wrist_y	Right leader-wrist y-position command.
8	leader_right_wrist/leader_right_wrist_z	Right leader-wrist z-position command.
9	leader_right_wrist/leader_right_wrist_roll	Right leader-wrist roll command.
10	leader_right_wrist/leader_right_wrist_pitch	Right leader-wrist pitch command.
11	leader_right_wrist/leader_right_wrist_yaw	Right leader-wrist yaw command.
12	leader_left_gripper/leader_left_gripper	Left leader-gripper command.
13	leader_right_gripper/leader_right_gripper	Right leader-gripper command.

The coordinate frame, units, orientation convention, and gripper-value range are inherited from the original teleoperation logging system. Users should preserve these conventions when training, normalizing, or replaying policies.

Follower State Vector: /observations/qpos

The 16-dimensional qpos vector contains 14 follower-arm joint positions and two follower-claw states.

Index	Dimension name	Description
0	follower_arm_joint_states/follower_left_shoulder_pitch_joint	Left shoulder pitch joint position.
1	follower_arm_joint_states/follower_left_shoulder_roll_joint	Left shoulder roll joint position.
2	follower_arm_joint_states/follower_left_shoulder_yaw_joint	Left shoulder yaw joint position.
3	follower_arm_joint_states/follower_left_elbow_joint	Left elbow joint position.
4	follower_arm_joint_states/follower_left_wrist_roll_joint	Left wrist roll joint position.
5	follower_arm_joint_states/follower_left_wrist_pitch_joint	Left wrist pitch joint position.
6	follower_arm_joint_states/follower_left_wrist_yaw_joint	Left wrist yaw joint position.
7	follower_arm_joint_states/follower_right_shoulder_pitch_joint	Right shoulder pitch joint position.
8	follower_arm_joint_states/follower_right_shoulder_roll_joint	Right shoulder roll joint position.
9	follower_arm_joint_states/follower_right_shoulder_yaw_joint	Right shoulder yaw joint position.
10	follower_arm_joint_states/follower_right_elbow_joint	Right elbow joint position.
11	follower_arm_joint_states/follower_right_wrist_roll_joint	Right wrist roll joint position.
12	follower_arm_joint_states/follower_right_wrist_pitch_joint	Right wrist pitch joint position.
13	follower_arm_joint_states/follower_right_wrist_yaw_joint	Right wrist yaw joint position.
14	follower_claw_joint_states/left_claw	Left follower-claw position.
15	follower_claw_joint_states/right_claw	Right follower-claw position.

Visual Observations

Some CareManip recording versions may include image observations in the HDF5 file. The camera keys, resolutions, encodings, and frame-synchronization method must be documented from the final released HDF5 schema before DOI generation.

Use the schema-inspection code below to identify all visual-observation keys in a representative episode. If RGB images are stored as compressed byte buffers, they must be decoded before use.

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Usage Example

Install the required packages:

pip install h5py numpy

Load and inspect one local HDF5 episode:

from pathlib import Path
import h5py

file_path = Path("data/pick_block/pick_block0000.hdf5")

with h5py.File(file_path, "r") as f:
    print("Top-level keys:", list(f.keys()))

    def show_tree(name, obj):
        if isinstance(obj, h5py.Dataset):
            print(f"{name}: shape={obj.shape}, dtype={obj.dtype}")

    f.visititems(show_tree)

    action = f["action"][:]
    qpos = f["observations/qpos"][:]

print("Action shape:", action.shape)  # expected: (T, 14)
print("qpos shape:", qpos.shape)      # expected: (T, 16)

Download a single episode from Hugging Face:

from huggingface_hub import hf_hub_download
import h5py

repo_id = "zw1213757576/CareManip"
filename = "data/pick_block/pick_block0000.hdf5"

local_path = hf_hub_download(
    repo_id=repo_id,
    repo_type="dataset",
    filename=filename,
)

with h5py.File(local_path, "r") as f:
    action = f["action"][:]
    qpos = f["observations/qpos"][:]

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Recommended Evaluation Protocol

CareManip is released as raw demonstrations. To ensure fair comparisons:

1. Split data by episode, never by individual frames.
2. Keep all frames from one HDF5 trajectory within the same split.
3. Report task-level performance and aggregate performance across the 15 task categories.
4. State whether models use actions, follower state, visual observations, language instructions, or a combination of modalities.
5. Report the preprocessing applied to action and qpos vectors, including normalization, resampling, filtering, clipping, and coordinate transformations.
6. Clearly distinguish in-distribution task performance from evaluation on unseen objects, scene configurations, or tasks.

The official train/validation/test split files will be added in a future update.

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Intended Uses

CareManip is intended for research and education in:

• Behavior cloning and robot imitation learning;
• Dual-arm manipulation and coordinated bimanual control;
• Visual and multimodal robot learning;
• Embodied AI and vision-language-action research;
• Assistive and care-oriented service robotics;
• Task-conditioned action prediction;
• Teleoperation analysis and leader-follower control modeling;
• Benchmark development for HDF5-based manipulation datasets.

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Limitations

• CareManip is collected in structured tabletop environments and does not by itself establish generalization to unseen homes, clinics, objects, users, robot platforms, or manipulation settings.
• The action representation is expressed in leader-wrist and leader-gripper space, while qpos represents the follower robot. Users must account for this representation difference when designing learning targets.
• The current release contains raw HDF5 files. Hugging Face's Dataset Viewer may not directly preview all HDF5 contents.
• Dataset quality, task-success labels, scene annotations, camera calibrations, and official data splits should be interpreted only from files and documentation included in the final public release.
• The dataset must not be used as the sole basis for safety-critical or clinical decision-making.

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Ethical and Privacy Considerations

Before public release, every HDF5 episode and associated metadata must be reviewed for personally identifiable information. This includes faces, names, speech, computer-screen contents, laboratory credentials, and other sensitive information.

The final accompanying paper should state the applicable ethics-review status, consent procedure, and data-sharing restrictions, if any.

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License

The CareManip dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

When using the dataset, users must cite both the dataset DOI and the associated paper.

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Citation

The Hugging Face DOI and paper citation will be inserted after the public archival release has been finalized.

@dataset{caremanip_2026,
  title     = {CareManip: A Teleoperation Dataset for Care-Oriented Tabletop Manipulation},
  author    = {REPLACE WITH AUTHOR LIST},
  year      = {2026},
  version   = {1.0},
  publisher = {Hugging Face},
  doi       = {REPLACE WITH HUGGING FACE DOI},
  url       = {https://huggingface.co/datasets/zw1213757576/CareManip}
}

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Contact

For questions, corrections, or collaboration requests, please use the repository discussion page or contact:

REPLACE WITH CONTACT EMAIL