Update README.md

README.md

@@ -1,20 +1,43 @@
-# WALL-OSS: Igniting VLMs toward the Embodied Space
+# WALL-OSS
 
 <div align="left">
 
-[![Paper](https://img.shields.io/badge/Paper-PDF-EA1B22?style=for-the-badge&logo=adobeacrobatreader&logoColor=fff)](https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf)
+<p align="center">
+    <img src="assets/logo.png" width="600"/>
+<p>
+
+<div align="center">
+
+[![Paper](https://img.shields.io/badge/📄%20Paper-PDF-EA1B22?style=for-the-badge&logo=adobeacrobatreader&logoColor=fff)](https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf)
+&nbsp;&nbsp;
 [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-x--square--robot-FFB000?style=for-the-badge&logo=huggingface&logoColor=000)](https://huggingface.co/x-square-robot)
+&nbsp;&nbsp;
 [![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge&logo=github&logoColor=fff)](https://github.com/X-Square-Robot/wall-x)
+&nbsp;&nbsp;
 [![Project Page](https://img.shields.io/badge/Project-1E90FF?style=for-the-badge&logo=google-chrome&logoColor=fff)](https://x2robot.com/en/research/68bc2cde8497d7f238dde690)
 
 </div>
 
-## 🤖 Model Description
+</div>
+
+## <a href="https://x2robot.cn-wlcb.ufileos.com/wall_oss.pdf" target="_blank"><strong>WALL-OSS: Igniting VLMs toward the Embodied Space</strong></a>
 
 We introduce **WALL-OSS**, an end-to-end embodied foundation model that leverages large-scale multimodal pretraining to achieve (1) embodiment-aware vision--language understanding, (2) strong language--action association, and (3) robust manipulation capability.
 Our approach employs a tightly coupled architecture and multi-strategies training curriculum that enables Unified Cross-Level CoT—seamlessly unifying instruction reasoning, subgoal decomposition, and fine-grained action synthesis within a single differentiable framework.
 Our results show that WALL-OSS attains high success on complex long-horizon manipulations, demonstrates strong instruction-following capabilities, complex   understanding and reasoning, and outperforms strong baselines, thereby providing a reliable and scalable path from VLMs to embodied foundation models.
 
+## 🎬 Video Demos
+
+<div align="center">
+    <video width="80%" controls>
+        <source src="https://x2robot.com/api/videos/file/wall-oss_top_720p-1.mp4" type="video/mp4">
+        Your browser does not support the video tag.
+    </video>
+    <p><strong>WALL-OSS in Action: Demonstrating advanced manipulation capabilities and embodied AI performance</strong></p>
+</div>
+
+
+
 ## 🚀 Quick Start
 
 ### Installation
@@ -41,7 +64,7 @@ import torch
 from wall_x.model.qwen2_5_based.modeling_qwen2_5_vl_act import Qwen2_5_VLMoEForAction
 
 # Load the model
-model_path = "X-Square-Robot/wall-oss-flow"  # or your local path
+model_path = "X-Square-Robot/wall-oss-fast"  # or your local path
 model = Qwen2_5_VLMoEForAction.from_pretrained(model_path)
 model.eval()