Update dataset card for EmbodiedCLBench

README.md

@@ -1,10 +1,65 @@
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 license: apache-2.0
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-数据集文件元信息以及数据文件，请浏览“数据集文件”页面获取。
 
-当前数据集卡片使用的是默认模版，数据集的贡献者未提供更加详细的数据集介绍，但是您可以通过如下GIT Clone命令，或者ModelScope SDK来下载数据集
+<p align="center"><sub>the first benchmark in embodied simulation for evaluating the continual learning capability of self-evolving agents</sub></p>
 
-#### 下载方法 
-:modelscope-code[]{type="sdk"}
-:modelscope-code[]{type="git"}
+<h1 align="center">EmbodiedCLBench: Evaluating Continual Learning for Self-Evolving Embodied Agents</h1>
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Paper-arXiv-b31b1b" alt="Paper">
+  <a href="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/"><img src="https://img.shields.io/badge/Project-Page-blue" alt="Project Page"></a>
+  <a href="https://huggingface.co/datasets/PKU-VaLuE-Lab/EmbodiedCLBench"><img src="https://img.shields.io/badge/Hugging%20Face-Dataset-yellow" alt="Hugging Face Dataset"></a>
+  <a href="https://github.com/PKU-VaLuE-Lab/EmbodiedCLBench"><img src="https://img.shields.io/badge/GitHub-Code-black" alt="GitHub Code"></a>
+</p>
+
+<p align="center">
+  <a href="https://jie-1203.github.io/">Jie Huang</a><sup>★,1,2</sup>&emsp;
+  <a href="https://github.com/YananChen8">Yanan Chen</a><sup>★,1</sup>&emsp;
+  <a href="https://liuruixun.github.io/">Ruixun Liu</a><sup>★,1</sup>&emsp;
+  <a href="https://hkc20.github.io/">Kaichen He</a><sup>1</sup>
+  <br>
+  <a href="https://github.com/hexinyi2101">Xinyi He</a><sup>1</sup>&emsp;
+  <a href="https://ruihuangai.github.io/">Rui Huang</a><sup>3</sup>&emsp;
+  <a href="https://zilongzheng.github.io/">Zilong Zheng</a><sup>2</sup>&emsp;
+  <a href="https://www.zlz.link/">Zhenliang Zhang</a><sup>2</sup>&emsp;
+  <a href="https://yiwuzhong.notion.site/homepage">Yiwu Zhong</a><sup>1,2,†</sup>
+</p>
+
+<p align="center">
+  <sup>1</sup>School of Intelligence Science and Technology, Peking University<br>
+  <sup>2</sup>State Key Laboratory of General Artificial Intelligence, BIGAI, Beijing, China<br>
+  <sup>3</sup>University of Hong Kong
+</p>
+
+<p align="center">★ Equal contribution.&emsp;† Corresponding author.</p>
+
+<p align="center">
+  <img src="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/static/images/paper/intro.png" alt="EmbodiedCLBench overview showing compositional generalization from basic to advanced tasks" width="100%">
+</p>
+
+**Core design:** we operationalize continual learning through compositional generalization: an agent first completes simpler tasks to accumulate experience, then is tested on whether it can recombine the acquired skills to solve more complex, unseen tasks.
+
+## Abstract
+
+Continual learning requires models to accumulate past experience and transfer it to new tasks. Despite substantial research on continual learning, existing work remains largely confined to non-embodied settings, without extending to embodied environments.
+
+To address this gap, we introduce EmbodiedCLBench, the first benchmark in embodied simulation for evaluating the continual learning capability of self-evolving agents. With compositional generalization as the core evaluation guidance, our benchmark evaluates whether agents can first learn from basic tasks and then re-combine the acquired skills to solve complex, unseen advanced tasks. Based on EmbodiedCLBench, we conduct extensive experiments across multiple agent harnesses and learning methods, revealing consistent limitations and distinctive behaviors.
+
+Our results show that while advanced tasks can be hardly solved under zero-shot setting, the experience from basic tasks enables consistent improvement across different harnesses. The benefit of experience remains stable as the task complexity increases. However, this improvement does not scale accordingly with the number of learning samples, and current agents cannot extract meaningful information from additional experience. Collectively, our benchmark provides a valuable resource for future research, and our findings highlight continual learning as a fundamental yet underexplored capability and offer insights for designing more effective self-evolving agents in embodied environments.
+
+## Benchmark Construction
+
+<p align="center">
+  <img src="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/static/images/paper/construction.png" alt="Overview of the EmbodiedCLBench construction process" width="100%">
+</p>
+
+<p align="center">Overview of the construction process</p>
+
+<p align="center">
+  <img src="https://pku-value-lab.github.io/EmbodiedCLBench-homepage/static/images/paper/statistics.png" alt="EmbodiedCLBench benchmark statistics" width="100%">
+</p>
+
+<p align="center">Benchmark statistics</p>
+
+## Citation