Update README.md

README.md

@@ -1,10 +1,79 @@
 ---
-tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+base_model:
+- declare-lab/nora-long
+datasets:
+- TomNickson/OpenX-Embodiment
+- jxu124/OpenX-Embodiment
+language:
+- en
+license: mit
+
+pipeline_tag: robotics
+---
+# NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
+
+[![Project Website](https://img.shields.io/badge/Project-Website-blue.svg)](https://declare-lab.github.io/nora-1.5)
+[![Model](https://img.shields.io/badge/Model-NORA--1.5-brightgreen)](https://huggingface.co/declare-lab/nora-1.5)
+[![arXiv](https://img.shields.io/badge/arXiv-2511.14659-b31b1b.svg)](https://arxiv.org/abs/2511.14659)
+![Status](https://img.shields.io/badge/Status-Active-orange)
+
+🔥 Project NORA is supported by Gemini and Lambda Labs! We are thankful to them.
+
+NORA-1.5 is a **Vision-Language-Action (VLA)** model that improves generalization and real-world decision making through **post-training with world-model-based and action-based preference rewards**.  
+The model builds upon the NORA foundation to achieve stronger **instruction following**, **closed-loop control**, and **real-robot success**, demonstrating reliability across **LIBERO** and **SimplerEnv** environments.
+
+This repository consolidates the full open-source release of **model checkpoints**, **inference code**, **training code**, and **evaluation tools**, along with documentation and examples.
+
+<p align="center">
+  <img src="https://declare-lab.github.io/assets/images/nora-1.5-arxiv-teaser.png" width="100%">
+</p>
+
+
+---
+
+## 🌐 Project Website
+
+🔗 **https://declare-lab.github.io/nora-1.5**
+ 
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+## 🚀 Key Features
+
+- **Vision-Language-Action architecture** with enhanced **task completion rate** and **distraction rate**
+- **Action-based preference optimization** using expert preference rewards  
+- **World-model-based preference learning** for improved planning and consistency  
+- Strong **closed-loop control**, enabling deployment in real robot settings  
+- Supports **multi-task**, **long-horizon**, and **few-shot generalization**  
+- Compatible with **LeRobot**, **LIBERO**, **SimplerEnv**, and custom environments  
+
+---
+
+## 📦 Repository Structure (will update)
+
+
+
+## 📆 TODO <a name="todos"></a>  ~ 1 week
+- [ ] Release the inference code of Nora-1.5
+- [ ] Release all relevant model checkpoints(Pretrained, libero, SimplerEnv etc)
+- [ ] Release the training/fine-tuning code of Nora-1.5 with LeRobot Dataset
+- [ ] Release SimplerEnv evaluation code 
+
+## Minimal Inference Sample (Will update)
+```python
+from inference.modelling_expert import VLAWithExpert
+
+model = VLAWithExpert() 
+model.to('cuda')
+outputs = model.sample_actions(PIL IMAGE,instruction,num_steps=10) ## Outputs 7 Dof action of normalized and unnormalized action
+```
+
+## Citation
+
+```bibtex
+@article{hung2025nora15,
+  title={NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-Based Preference Rewards},
+  author={Hung, Chia-Yu and Majumder, Navonil and Deng, Haoyuan, Liu Renhang, Yankang Ang, Amir Zadeh, Chuan Li, Dorien Herremans, Ziwei Wang, and Soujanya Poria},
+  journal={arXiv preprint},
+  year={2025}
+}
+```