update README

README.md

@@ -4,6 +4,8 @@
 
 </div>
 
+<div align="center">
+
 [![Paper InternData-A1](https://img.shields.io/badge/Paper-InternData--A1-red.svg)](https://arxiv.org/abs/2511.16651)
 [![Paper Nimbus](https://img.shields.io/badge/Paper-Nimbus-red.svg)](https://arxiv.org/abs/2601.21449)
 [![Paper InternVLA-M1](https://img.shields.io/badge/Paper-InternVLA--M1-red.svg)](https://arxiv.org/abs/2510.13778)
@@ -11,10 +13,15 @@
 [![Data InternData-M1](https://img.shields.io/badge/Data-InternData--M1-blue?logo=huggingface)](https://huggingface.co/datasets/InternRobotics/InternData-M1)
 [![Docs](https://img.shields.io/badge/Docs-TBD-lightgrey.svg)](#)
 
+</div>
+
 ## 💻 About
 
-InternDataEngine is a data-centric engine for embodied AI that powers large-scale model training and iteration.  
-Built on NVIDIA Isaac Sim, it unifies high-fidelity physical interaction from InternData-A1, semantic task and scene generation from InternData-M1, and high-throughput scheduling from the Nimbus framework to deliver realistic, task-aligned, and massively scalable robotic manipulation data.
+<div align="center">
+  <img src="./docs/images/intern_data_engine.jpeg" alt="InternDataEngine Overview" width="80%">
+</div>
+
+InternDataEngine is a synthetic data generation engine for embodied AI that powers large-scale model training and iteration. Built on NVIDIA Isaac Sim, it unifies high-fidelity physical interaction from InternData-A1, semantic task and scene generation from InternData-M1, and high-throughput scheduling from the Nimbus framework to deliver realistic, task-aligned, and massively scalable robotic manipulation data.
 
 - **More realistic physical interaction**: Unified simulation of rigid, articulated, deformable, and fluid objects across single-arm, dual-arm, and humanoid robots, enabling long-horizon, skill-composed manipulation that better supports sim-to-real transfer.
 - **More task-aligned data generation**: LLM-driven task and instruction generation with task-oriented scene graphs (ToSG), producing structured scenes and rich multi-modal annotations (boxes, keypoints, trajectories) for complex instruction-following and spatial reasoning.
@@ -55,3 +62,13 @@ All the code within this repo are under [CC BY-NC-SA 4.0](https://creativecommon
   year={2025}
 }
 ```
+
+<!--
+```BibTeX
+@misc{interndataengine2026,
+  title={InternDataEngine: A Synthetic Data Generation Engine for Robotic Learning},
+  author={InternDataEngine Contributors},
+  year={2026},
+  }
+}
+``` -->