Add pipeline tag: robotics

README.md

@@ -1,36 +1,38 @@
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-license: apache-2.0
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-# CoIRL-AD: Collaborative–Competitive Imitation–Reinforcement Learning in Latent World Models for Autonomous Driving
-
-<div align="center">
-<a href="https://seu-zxj.github.io/">Xiaoji Zheng</a>*, 
-<a href="https://ziyuan-yang.github.io">Yangzi Yuan</a>*, 
-<a href="https://github.com/Ian-cyh">Yanhao Chen</a>, 
-<a href="https://github.com/doraemonaaaa">Yuhang Peng</a>, 
-<a href="https://github.com/TANGXTONG1">Yuanrong Tang</a>, 
-<a href="https://openreview.net/profile?id=~Gengyuan_Liu1">Gengyuan Liu</a>,
-<a href="https://scholar.google.com/citations?user=_Wrx_yEAAAAJ">Bokui Chen</a>‡ and 
-<a href="https://scholar.google.com/citations?user=AktmI14AAAAJ">Jiangtao Gong</a>‡.
-<div>
-*: Equal contribution.
-‡: Corresponding authors.
-</div>
-
-<div>
-<a href="https://seu-zxj.github.io/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/_-page-blue?style=flat&logo=githubpages&logoColor=white&logoSize=auto&labelColor=gray"></a>
-<a href="https://arxiv.org/abs/2510.12560"><img alt="Static Badge" src="https://img.shields.io/badge/arxiv-paper-red?logo=arxiv"></a>
-<a href="https://github.com/SEU-zxj/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/github-code-white?logo=github"></a>
-<a href="https://huggingface.co/Student-Xiaoji/CoIRL-AD-models"><img alt="Static Badge" src="https://img.shields.io/badge/hf-models-yellow?logo=huggingface"></a>
-</div>
-
-</div>
-
-**CoIRL-AD** introduces a dual-policy framework that unifies imitation learning (IL) and reinforcement learning (RL) through a collaborative–competitive mechanism within a latent world model.  
-The framework enhances generalization and robustness in end-to-end autonomous driving without relying on external simulators.
-
-![main figure](./assets/main_figure.jpg)
-
-Here we provide our model checkpoints (see `/ckpts`), info files (see `/info-files`) for dataloader to download and reproduce our experiment results.
-
+---
+license: apache-2.0
+pipeline_tag: robotics
+---
+
+# CoIRL-AD: Collaborative–Competitive Imitation–Reinforcement Learning in Latent World Models for Autonomous Driving
+
+<div align="center">
+<a href="https://seu-zxj.github.io/">Xiaoji Zheng</a>*, 
+<a href="https://ziyuan-yang.github.io">Yangzi Yuan</a>*, 
+<a href="https://github.com/Ian-cyh">Yanhao Chen</a>, 
+<a href="https://github.com/doraemonaaaa">Yuhang Peng</a>, 
+<a href="https://github.com/TANGXTONG1">Yuanrong Tang</a>, 
+<a href="https://openreview.net/profile?id=~Gengyuan_Liu1">Gengyuan Liu</a>,
+<a href="https://scholar.google.com/citations?user=_Wrx_yEAAAAJ">Bokui Chen</a>‡ and 
+<a href="https://scholar.google.com/citations?user=AktmI14AAAAJ">Jiangtao Gong</a>‡.
+<div>
+*: Equal contribution.
+‡: Corresponding authors.
+</div>
+
+<div>
+<a href="https://seu-zxj.github.io/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/_-page-blue?style=flat&logo=githubpages&logoColor=white&logoSize=auto&labelColor=gray"></a>
+<a href="https://arxiv.org/abs/2510.12560"><img alt="Static Badge" src="https://img.shields.io/badge/arxiv-paper-red?logo=arxiv"></a>
+<a href="https://github.com/SEU-zxj/CoIRL-AD"><img alt="Static Badge" src="https://img.shields.io/badge/github-code-white?logo=github"></a>
+<a href="https://huggingface.co/Student-Xiaoji/CoIRL-AD-models"><img alt="Static Badge" src="https://img.shields.io/badge/hf-models-yellow?logo=huggingface"></a>
+</div>
+
+</div>
+
+**CoIRL-AD** introduces a dual-policy framework that unifies imitation learning (IL) and reinforcement learning (RL) through a collaborative–competitive mechanism within a latent world model.  
+The framework enhances generalization and robustness in end-to-end autonomous driving without relying on external simulators.
+
+![main figure](./assets/main_figure.jpg)
+
+Here we provide our model checkpoints (see `/ckpts`), info files (see `/info-files`) for dataloader to download and reproduce our experiment results.
+
 ## For more details, please refer to our [github repo](https://github.com/SEU-zxj/CoIRL-AD).