Update README.md

README.md

@@ -1,63 +1,69 @@
 ---
-{}
+license: cc-by-4.0
 ---
 
+
 # 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 track high-quality and precise human hand motion and manipulation.
+
+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
-- **Total episodes:** 6
-- **Frames:** 7205   
-- **Tasks:** 2 distinct industrial manipulation tasks
-- **Modalities:** RGB, 3D Hand Keypoints, Wrist Pose, Head Pose, Language Annotations
-- **Resolution and rate:** 960 x 720 (headcam), 480 x 640 (wristcam) @ 30 FPS
-- **Format:** LeRobot Dataset v3
 
-## How to Use
+| 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 | LeRobot Dataset v3 |
 
-**Quick Example:**
+## Quick Start
 ```python
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
-EPISODE_ID = 0
-SAMPLE_ID = 10
-
-dataset = LeRobotDataset(repo_id="aprilrobotics/sample", episodes=[EPISODE_ID])
-frame = dataset[SAMPLE_ID]
+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: [head_pose(7), wrist_pose(7), hand_keypoints(63)]
-#   pose = [x, y, z, qx, qy, qz, qw]
+# State vector: [head_pose(7), wrist_pose(7), hand_keypoints(63)]
 state = frame["observation.state"].numpy()
-head_pose = state[0:7]  # position + quaternion (xyzw)
-wrist_pose = state[7:14]  # position + quaternion (xyzw)
 
-# 21 hand keypoints:
-#   0=wrist, 1-4=thumb, 5-8=index, 9-12=middle, 13-16=ring, 17-20=pinky
+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 a visualization in rerun for our data. Visit [https://github.com/AprilRoboticsAI/Visualization] and follow the instructions there.
+## Visualization
+
+We provide visualization using Rerun. See the [visualization repository](https://github.com/AprilRoboticsAI/Visualization) 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
 
-## Intended uses
+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.
 
-* Vision-Language-Action Model Pre/Post-Training
-* World Model Pre/Post-Trainig
-* Video Generation Training
-* VR/AR/XR Applications
-* Hand Tracking Model Training
+### Additional Services
 
-# About April Robotics
+April Robotics captures ground truth hand tracking data using proprietary sensorized wearables. We can provide:
 
-We view humans as another embodiment of a humanoid robots and believe their behavior should be captured with the same fidelity as robotic data. By building wearables like 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. This tight integration ensures the data directly translates into better downstream robotic capabilities. 
+- Accurate human expert data from partner factories across industries
+- Additional modalities: touch, depth, audio
+- On-demand high-quality data collection for custom training needs
 
-## Services
+## Contact
 
-April Robotics can provide:
-- Human expert data from factories across industries
-- Additional data modalities: Touch, Depth, Audio
-- 
+📧 team@aprilrobotics.ai