Rewrite dataset card title and description

README.md

@@ -8,9 +8,9 @@ tags:
   - robox
 ---
 
-# RoboX-EgoTask-v1.7
+# RoboX-EgoTask
 
-17 clips from 5 recordings, collected via the RoboX.
+A premium egocentric dataset of real-world hand and object task demonstrations, recorded in first person through RoboX. This release bundles 17 task clips drawn from 5 full recordings (about 6 minutes of footage), each pairing clean RGB video with synchronized, robotics ready annotations: MediaPipe hand keypoints (21 joints, 2D and 3D), 6DoF camera pose, IMU, depth metadata, body pose, person segmentation, and per frame trajectory and billing signals. It is built for embodied AI and egocentric robotics research, with contributor aware train, validation and test splits and a motion quality score on every clip.
 
 ## Dataset Summary
 
@@ -236,7 +236,7 @@ This dataset is intended for embodied AI and egocentric robotics research.
 import json
 from pathlib import Path
 
-root = Path("RoboX-EgoTask-v1.7")
+root = Path("RoboX-EgoTask")
 clips = [json.loads(line) for line in (root / "metadata" / "clips.jsonl").read_text().splitlines()]
 # Filter clips that have hand keypoints in this export tier
 hand_clips = [c for c in clips if (c.get("exported_modalities") or {}).get("hand_keypoints_2d")]
@@ -246,11 +246,11 @@ print(clips[0]["clip_id"], clips[0]["labels"])
 ## Citation
 
 ```bibtex
-@dataset{robox_RoboX-EgoTask-v1.7_2026,
-  title={RoboX-EgoTask-v1.7: Egocentric EgoTask Dataset},
+@dataset{robox_egotask_2026,
+  title={RoboX-EgoTask: An Egocentric Hand-Object Task Dataset},
   author={RoboX Team},
   year={2026},
-  url={https://huggingface.co/datasets/robox-data/RoboX-EgoTask-v1.7},
+  url={https://huggingface.co/datasets/RoboXTechnologies/RoboX-EgoTask},
   license={CC-BY-NC-4.0}
 }
 ```