Datasets:
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episode_index
int64 | frame_index
int64 | timestamp
float32 | task_index
int64 | index
int64 | observation.state.hand_joints
list | observation.state.finger_angles
list | observation.state.gestures
list | observation.state.imu
list |
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End of preview. Expand
in Data Studio
manus-egocentric-sample
Egocentric video dataset with Manus glove hand tracking data, converted to LeRobot v3.0 format.
Dataset Description
This dataset contains egocentric (first-person view) recordings of human hands performing various manipulation tasks, captured with:
- Manus Metagloves: High-precision finger tracking (~70Hz)
- OAK-D Camera: RGB video (1920x1080, 30fps) + Depth (640x400, 30fps)
- IMU: Accelerometer and gyroscope data
Dataset Statistics
| Property | Value |
|---|---|
| Total Episodes | 20 |
| Total Frames | 96,544 |
| FPS | 30 |
| Robot Type | manus_gloves |
| LeRobot Version | v2.1 |
| Dataset Size | ~15.3 GB |
Tasks
Pick_and_pack_task, fold_laundry
Features
| Feature | Type | Shape |
|---|---|---|
episode_index |
int64 | [1] |
frame_index |
int64 | [1] |
timestamp |
float32 | [1] |
task_index |
int64 | [1] |
observation.images.egocentric |
video | [1080, 1920, 3] |
observation.state.hand_joints |
float32 | [150] |
observation.state.finger_angles |
float32 | [40] |
observation.state.gestures |
float32 | [26] |
observation.state.imu |
float32 | [6] |
Depth Data
This dataset includes raw depth data as a custom extension (LeRobot v3.0 doesn't officially support depth yet).
| Property | Value |
|---|---|
| Format | Raw uint16 binary |
| Resolution | [400, 640] (H, W) |
| Unit | millimeters |
| Episodes with depth | 17 |
| Storage | Episode-based (depth/episode_XXXXXX.bin) |
To load depth data:
import numpy as np
def load_depth(dataset_path, episode_index):
depth_path = dataset_path / f"depth/episode_{episode_index:06d}.bin"
if depth_path.exists():
data = np.fromfile(depth_path, dtype=np.uint16)
# Reshape based on frame count (need timestamps)
return data
return None
Usage
Load with LeRobot
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset("opengraph-labs/manus-egocentric-sample")
# Access a sample
sample = dataset[0]
print(sample.keys())
# dict_keys(['observation.images.egocentric', 'observation.state.hand_joints', ...])
# Get hand joint positions (25 joints x 3 coords x 2 hands = 150 dims)
hand_joints = sample["observation.state.hand_joints"]
print(hand_joints.shape) # torch.Size([150])
Data Structure
manus-egocentric-sample/
├── meta/
│ ├── info.json # Dataset schema and features
│ ├── stats.json # Feature statistics (mean/std/min/max)
│ ├── tasks.jsonl # Task definitions
│ ├── episodes.jsonl # Episode metadata
│ └── episodes/ # Episode info (parquet)
├── data/chunk-XXX/ # Time-series data (parquet)
├── videos/ # RGB video (mp4)
└── depth/ # Raw depth data (uint16 binary)
Hand Skeleton Structure
Each hand has 25 tracked joints:
Hand (Root)
├── Thumb: MCP → PIP → DIP → TIP (4 joints)
├── Index: MCP → IP → PIP → DIP → TIP (5 joints)
├── Middle: MCP → IP → PIP → DIP → TIP (5 joints)
├── Ring: MCP → IP → PIP → DIP → TIP (5 joints)
└── Pinky: MCP → IP → PIP → DIP → TIP (5 joints)
Citation
If you use this dataset, please cite:
@dataset{manus_egocentric_2025,
title = {Manus Egocentric Hand Tracking Dataset},
author = {OpenGraph Labs},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/datasets/opengraph-labs/manus-egocentric-sample}
}
License
This dataset is released under the Apache 2.0 License.
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