ViTaMIn-OXT

ViTaMIn-OXT is an Open X-Tactile (OXT)-formatted derivative of the public ViTaMIn dataset. It follows the FTP-1 data-processing schema and contains synchronized wrist RGB, dual-pad ViTaMIn tactile images, wrist pose, gripper opening width, and task instructions.

Dataset summary

Sub-dataset	Task	Trajectories	Frames
Articulated Object Manipulation	Manipulate the articulated object	75	22,848
Dynamic Peg Insertion	Insert the peg into the moving target	129	33,173
Orange Placement	Pick up the orange and place it on the plate	110	26,037
Scissor Hanging	Pick up the scissors and hang them on the hook	134	81,255
Sponge Insertion	Pick up the sponge and insert it into the cup	138	78,901
Test Tube Reorientation	Pick up the test tube and reorient it	161	119,125
Total	6 tasks	747	361,339

The data are single-hand, robot-free UMI-style demonstrations with one wrist camera and two image-based tactile pads. Version 1.0.0 uses the latest local ViTaMIn release supplied by the contributor.

Repository layout

ViTaMIn-OXT/
├── Articulated_Object_Manipulation/
│   └── Articulated_Object_Manipulation_dataset.zarr.zip
├── Dynamic_Peg_Insertion/
│   └── Dynamic_Peg_Insertion_dataset.zarr.zip
├── Orange_Placement/
│   └── Orange_Placement_dataset.zarr.zip
├── Scissor_Hanging/
│   └── Scissor_Hanging_dataset.zarr.zip
├── Sponge_Insertion/
│   └── Sponge_Insertion_dataset.zarr.zip
├── Test_Tube_Reorientation/
│   └── Test_Tube_Reorientation_dataset.zarr.zip
├── README.md
└── SHA256SUMS

Each ZIP is a Zarr v2 store whose root contains data/ and meta/. The archive is ready to open directly; do not add another directory level around the Zarr root when repacking.

Main fields

Key	Shape	Dtype	Meaning
data/timestamps	[T]	float64	Synthetic per-episode seconds at 30 FPS
data/right_wrist_camera_rgb	[T, 224, 224, 3]	uint8	Wrist RGB image
data/right_wrist_pose	[T, 6]	float32	End-effector position XYZ followed by axis-angle rotation XYZ
data/right_wrist_demo_start_pose	[T, 6]	float64	Demonstration start pose
data/right_wrist_demo_end_pose	[T, 6]	float64	Demonstration end pose
data/right_hand_joints	[T, 1]	float32	Parallel-gripper opening width in metres; larger means more open
data/right_hand_joints_idx	[T, 1]	int32	Constant FTP-1 hand/gripper slot ID 28
data/right_tactile_data_gripper_left_pad	[T, 1, 224, 224, 3]	uint8	Left tactile-pad image
data/right_tactile_data_gripper_right_pad	[T, 1, 224, 224, 3]	uint8	Right tactile-pad image
data/right_tactile_area_gripper_left_pad	[T, 1]	int32	Constant functional-area ID 0
data/right_tactile_area_gripper_right_pad	[T, 1]	int32	Constant functional-area ID 1
data/right_tactile_sensor_gripper_{left,right}_pad	[T]	string	Constant sensor name ViTaMIn
data/right_tactile_type_gripper_{left,right}_pad	[T]	string	Constant tactile type image
data/sub_task_instruction	[T]	string	Natural-language task instruction
meta/episode_ends	[E]	int64	Exclusive cumulative end index of each trajectory

The source archives contain no timestamp stream. Therefore, timestamps were deterministically generated as frame_index_within_episode / 30.0; they reset to zero at every trajectory boundary. They are suitable for 30 FPS playback/alignment but are not hardware capture timestamps.

right_hand_joints_idx = 28 is a schema slot identifier (zero-based slot 28 in the canonical 32-D FTP-1 hand/gripper space), not a physical joint number. The corresponding scalar is copied without rescaling from source robot0_gripper_width; its observed dataset range is approximately 0 to 0.0734371 m.

Loading an archive

Use Zarr 2.x and register the JPEG XL codec before opening image arrays:

pip install "zarr<3" imagecodecs

import zarr

try:
    from imagecodecs.numcodecs import register_codecs
except ImportError:
    from imagecodecs_numcodecs import register_codecs

register_codecs(verbose=False)

archive = "Orange_Placement/Orange_Placement_dataset.zarr.zip"
store = zarr.ZipStore(archive, mode="r")
root = zarr.open_group(store=store, mode="r")

print(root["data/right_wrist_camera_rgb"].shape)
print(root["data/right_tactile_data_gripper_left_pad"].shape)
print(root["meta/episode_ends"][:])

store.close()

Verify downloaded files from the repository root with:

sha256sum -c SHA256SUMS

Conversion and quality checks

The conversion copied encoded image chunks byte-for-byte, read back numerical arrays exactly, and ran the OXT metadata/schema precheck. All six sub-datasets passed with no failed datasets or warnings. ZIP integrity and checksums were also verified. The contributor completed the required 4D coordinate/trajectory inspection and manually reviewed the VLM-generated RGB+tactile montages; no fatal synchronization, corruption, task-label, or tactile-channel issue remained.

License and citation

This formatted derivative follows the MIT license declared by the source dataset. Please also consult and cite the original ViTaMIn work:

@article{liu2025vitamin,
  title   = {ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface},
  author  = {Liu, Fangchen and Li, Chuanyu and Qin, Yihua and Xu, Jing and Abbeel, Pieter and Chen, Rui},
  journal = {arXiv preprint arXiv:2504.06156},
  year    = {2025}
}

Project page: https://chuanyune.github.io/ViTaMIn_page/
OXT format and contribution information: https://open-x-tactile.github.io/
Contact: chuanyu.ne79@gmail.com