Update README.md

README.md

@@ -60,11 +60,10 @@ Each frame contains:
 > **Note:** Isaac Lab Forge PegInsert uses `collapse_obs_dict` — all observations are compressed into a flat 24-tensor. Force/Torque is extracted separately via `env.unwrapped.force_sensor_smooth`.
 
 ## Quick Start
-
 ```python
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 
-dataset = LeRobotDataset("exokern/contactbench-forge-peginsert-v0")
+dataset = LeRobotDataset("EXOKERN/contactbench-forge-peginsert-v0")
 print(f"Episodes: {dataset.num_episodes}, Frames: {len(dataset)}")
 
 # Access a frame
@@ -89,7 +88,6 @@ print(f"Force: {wrench[:3]} N, Torque: {wrench[3:]} Nm")
 - **Domain Randomization:** Default FORGE PegInsert parameters
 
 ## Reproduction
-
 ```bash
 # 1. Train policy (~20-30 min on RTX 4090)
 ./isaaclab.sh -p scripts/reinforcement_learning/rl_games/train.py \
@@ -116,14 +114,15 @@ Commercial licensing available from EXOKERN.
 We produce industrially calibrated force/torque manipulation data for enterprise robotics.
 Contact-rich. Sensor-annotated. Industrially validated.
 
-## Citation
+🌐 [exokern.com](https://exokern.com) · 🤗 [huggingface.co/EXOKERN](https://huggingface.co/EXOKERN)
 
+## Citation
 ```bibtex
 @dataset{exokern_contactbench_v0_2026,
   title={EXOKERN ContactBench v0: Peg Insertion with Force/Torque},
   author={EXOKERN},
   year={2026},
   publisher={Hugging Face},
-  url={https://huggingface.co/datasets/exokern/contactbench-forge-peginsert-v0}
+  url={https://huggingface.co/datasets/EXOKERN/contactbench-forge-peginsert-v0}
 }
-```
+```