<?xml version="1.0" encoding="utf-8" standalone="yes" ?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>Embodied AI | Borui Li</title>
    <link>https://www.liborui.cn/tag/embodied-ai/</link>
      <atom:link href="https://www.liborui.cn/tag/embodied-ai/index.xml" rel="self" type="application/rss+xml" />
    <description>Embodied AI</description>
    <generator>Wowchemy (https://wowchemy.com)</generator><language>en-us</language><lastBuildDate>Thu, 02 Jul 2026 17:58:28 +0000</lastBuildDate>
    <image>
      <url>https://www.liborui.cn/media/icon_hu081278e7bced6d30450a48e8c97864fb_558031_512x512_fill_lanczos_center_3.png</url>
      <title>Embodied AI</title>
      <link>https://www.liborui.cn/tag/embodied-ai/</link>
    </image>
    
    <item>
      <title>Embodied.cpp</title>
      <link>https://www.liborui.cn/project/embodied-cpp/</link>
      <pubDate>Thu, 02 Jul 2026 17:58:28 +0000</pubDate>
      <guid>https://www.liborui.cn/project/embodied-cpp/</guid>
      <description>&lt;p&gt;Embodied.cpp is a portable C++ inference runtime for embodied AI models, including vision-language-action (VLA) and world-action models. It provides a shared deployment path across heterogeneous CPUs, GPUs, NPUs, robots, and simulators without requiring a separate model-specific Python stack for every model.&lt;/p&gt;
&lt;h2 id=&#34;overview&#34;&gt;Overview&lt;/h2&gt;
&lt;p&gt;Embodied AI models combine multimodal perception, reasoning, and control, but their deployment is often fragmented across Python environments, model-specific runtime code, and hardware-specific execution paths. Embodied.cpp turns this into a unified systems stack: it converts supported models to GGUF, exposes a common runtime architecture, and provides reusable serving and evaluation paths.&lt;/p&gt;
&lt;p&gt;Project leads: &lt;strong&gt;Borui Li&lt;/strong&gt; and &lt;strong&gt;Hao Wu&lt;/strong&gt;.&lt;/p&gt;
&lt;h2 id=&#34;highlights&#34;&gt;Highlights&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;Portable deployment:&lt;/strong&gt; a common C++ execution path across heterogeneous robots, edge devices, and simulators.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Model-aware runtime:&lt;/strong&gt; reusable input adapters, sequence builders, backbone execution, head plugins, and deployment adapters.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;GGUF workflow:&lt;/strong&gt; conversion and quantization tools for supported embodied models, with ready-to-use model artifacts on Hugging Face.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;Evaluation-ready:&lt;/strong&gt; integrated workflows for LIBERO and RoboTwin.&lt;/li&gt;
&lt;/ul&gt;
&lt;h2 id=&#34;supported-models&#34;&gt;Supported Models&lt;/h2&gt;
&lt;table&gt;
&lt;thead&gt;
&lt;tr&gt;
&lt;th&gt;Model family&lt;/th&gt;
&lt;th&gt;Current support&lt;/th&gt;
&lt;/tr&gt;
&lt;/thead&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;VLA&lt;/td&gt;
&lt;td&gt;pi0.5 and HY-VLA&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;World-action model&lt;/td&gt;
&lt;td&gt;Preliminary LingBot-VA path&lt;/td&gt;
&lt;/tr&gt;
&lt;tr&gt;
&lt;td&gt;Deployment backends&lt;/td&gt;
&lt;td&gt;CPU, CUDA GPU, and heterogeneous edge hardware&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;
&lt;p&gt;The project is actively expanding model coverage and deployment backends. See the &lt;a href=&#34;https://github.com/SEU-PAISys/Embodied.cpp#-current-support-and-roadmap&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;repository roadmap&lt;/a&gt; for the current status.&lt;/p&gt;
&lt;h2 id=&#34;evaluation&#34;&gt;Evaluation&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;&lt;strong&gt;LIBERO:&lt;/strong&gt; simulation workflows for manipulation tasks and smoke tests.&lt;/li&gt;
&lt;li&gt;&lt;strong&gt;RoboTwin:&lt;/strong&gt; native C++ execution for dual-arm robotic manipulation evaluation.&lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;Detailed setup and evaluation instructions are maintained in the &lt;a href=&#34;https://github.com/SEU-PAISys/Embodied.cpp&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;project repository&lt;/a&gt;.&lt;/p&gt;
&lt;h2 id=&#34;quick-start&#34;&gt;Quick Start&lt;/h2&gt;
&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; class=&#34;chroma&#34;&gt;&lt;code class=&#34;language-bash&#34; data-lang=&#34;bash&#34;&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;git clone https://github.com/SEU-PAISys/Embodied.cpp.git
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;&lt;span class=&#34;nb&#34;&gt;cd&lt;/span&gt; Embodied.cpp
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;./patches/init_third_party.sh
&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;p&gt;Then choose the model and backend you need, download a compatible GGUF checkpoint from the &lt;a href=&#34;https://huggingface.co/SEU-PAISys/Embodied.cpp&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;model collection&lt;/a&gt;, and follow the repository&amp;rsquo;s build instructions for CPU or CUDA execution.&lt;/p&gt;
&lt;h2 id=&#34;model-conversion&#34;&gt;Model Conversion&lt;/h2&gt;
&lt;p&gt;The repository includes conversion and quantization scripts for pi0.5, HY-VLA, and LingBot-VA. If you are adapting a new checkpoint, start from the scripts in &lt;a href=&#34;https://github.com/SEU-PAISys/Embodied.cpp/tree/main/scripts&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;&lt;code&gt;scripts/&lt;/code&gt;&lt;/a&gt;. For standard use, the pre-converted GGUF releases are the fastest path.&lt;/p&gt;
&lt;h2 id=&#34;citation&#34;&gt;Citation&lt;/h2&gt;
&lt;div class=&#34;highlight&#34;&gt;&lt;pre tabindex=&#34;0&#34; class=&#34;chroma&#34;&gt;&lt;code class=&#34;language-bibtex&#34; data-lang=&#34;bibtex&#34;&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;&lt;span class=&#34;nc&#34;&gt;@article&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;{&lt;/span&gt;&lt;span class=&#34;nl&#34;&gt;xu2026embodiedcpp&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;title&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots}&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;author&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{Xu, Ling and Han, Chuyu and Li, Borui and Wu, Hao and Jiang, Shiqi and Cao, Ting and Li, Chuanyou and Zhong, Sheng and Wang, Shuai}&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;journal&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{arXiv preprint arXiv:2607.02501}&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;year&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{2026}&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;doi&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{10.48550/arXiv.2607.02501}&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;,&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;  &lt;span class=&#34;na&#34;&gt;url&lt;/span&gt;&lt;span class=&#34;p&#34;&gt;=&lt;/span&gt;&lt;span class=&#34;s&#34;&gt;{https://arxiv.org/abs/2607.02501}&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;span class=&#34;line&#34;&gt;&lt;span class=&#34;cl&#34;&gt;&lt;span class=&#34;p&#34;&gt;}&lt;/span&gt;
&lt;/span&gt;&lt;/span&gt;&lt;/code&gt;&lt;/pre&gt;&lt;/div&gt;&lt;h2 id=&#34;acknowledgements&#34;&gt;Acknowledgements&lt;/h2&gt;
&lt;p&gt;Embodied.cpp builds on and interoperates with &lt;a href=&#34;https://github.com/ggml-org/llama.cpp&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;llama.cpp&lt;/a&gt;, &lt;a href=&#34;https://github.com/VinRobotics/vla.cpp&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;vla.cpp&lt;/a&gt;, &lt;a href=&#34;https://github.com/Physical-Intelligence/openpi&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;OpenPI&lt;/a&gt;, &lt;a href=&#34;https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;HY-VLA&lt;/a&gt;, &lt;a href=&#34;https://github.com/robbyant/lingbot-vla&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;LingBot-VA&lt;/a&gt;, &lt;a href=&#34;https://github.com/Lifelong-Robot-Learning/LIBERO&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;LIBERO&lt;/a&gt;, and &lt;a href=&#34;https://github.com/RoboTwin-Platform/RoboTwin&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;RoboTwin&lt;/a&gt;. Each upstream project and model remains subject to its own license.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Embodied.cpp: A Portable Inference Runtime of Embodied AI Models on Heterogeneous Robots</title>
      <link>https://www.liborui.cn/publication/39-arxiv26-embodied-cpp/</link>
      <pubDate>Thu, 02 Jul 2026 17:58:28 +0000</pubDate>
      <guid>https://www.liborui.cn/publication/39-arxiv26-embodied-cpp/</guid>
      <description></description>
    </item>
    
  </channel>
</rss>
