Search is not available for this dataset
observation.state
list | timestamp
float32 | frame_index
int64 | episode_index
int64 | index
int64 | task_index
int64 |
|---|---|---|---|---|---|
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|
End of preview. Expand
in Data Studio
April Robotics Data Sample
This dataset contains 3D ground truth hand and finger annotations combined with egocentric and wrist recordings of industrial assembly operations captured in an active manufacturing environment. The data showcases our multimodal capture system consisting of head and wrist-mounted cameras and a sensorized glove that tracks high-quality, precise human hand motion and manipulation.
Dataset Summary
| Property | Value |
|---|---|
| Total episodes | 6 |
| Frames | 7,205 |
| Tasks | 2 distinct industrial manipulation tasks |
| Modalities | RGB, 3D Hand Keypoints, Wrist Pose, Head Pose, Language Annotations |
| Resolution | 960×720 (head cam), 480×640 (wrist cam) @ 30 FPS |
| Format | LeRobotDataset v3.0 |
Quick Start
from lerobot.datasets.lerobot_dataset import LeRobotDataset
dataset = LeRobotDataset(repo_id="aprilrobotics/sample", episodes=[0])
frame = dataset[10]
# Camera images (H, W, 3)
head_img = frame["observation.images.head_cam"].numpy().transpose(1, 2, 0)
wrist_img = frame["observation.images.wrist_cam"].numpy().transpose(1, 2, 0)
# State vector: [head_pose(7), wrist_pose(7), hand_keypoints(63)]
state = frame["observation.state"].numpy()
head_pose = state[0:7] # position (xyz) + quaternion (xyzw)
wrist_pose = state[7:14] # position (xyz) + quaternion (xyzw)
# 21 hand keypoints (xyz each):
# 0=wrist, 1-4=thumb, 5-8=index, 9-12=middle, 13-16=ring, 17-20=pinky
keypoints = state[14:].reshape(21, 3)
Visualization
We provide visualization using Rerun. See the visualization repository for instructions.
Intended Uses
- Vision-Language-Action model pre/post-training
- World model pre/post-training
- Video generation training
- VR/AR/XR applications
- Hand tracking model training
About April Robotics
We view humans as another embodiment of humanoid robots and believe their behavior should be captured with the same fidelity as robotic data. By building wearables such as sensorized gloves and using the data ourselves to train and deploy humanoids, we create a closed loop where real-world use continuously improves data quality.
What We Can Offer
April Robotics captures ground truth hand tracking data using proprietary sensorized wearables. We can provide:
- Accurate human expert data (hands, head, body) from partner factories across industries
- Additional modalities: touch, depth, audio
- On-demand high-quality data collection for custom training needs
Contact
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