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  <meta name="description" content="RabbitRobot VLN Lab: real-robot VLN/VLR for AMR, with deviation detection, verifiable recovery, ROS 2, Nav2, mapping and public robot assets.">
  <title>RabbitRobot VLN Lab</title>
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<body>
  <header class="hero">
    <div class="hero-inner">
      <p class="kicker">RabbitRobot VLN / VLR Lab</p>
      <h1>真实 AMR 上的长程导航与可验证恢复</h1>
      <p>RabbitRobot 是我的真实机器人研究平台：把 ROS 2、Nav2、地图、传感器、Web 工具和公开数据资产连接起来，服务 Vision-Language Navigation / Vision-Language Recovery。</p>
      <div class="hero-actions">
        <a class="button primary" href="https://huggingface.co/datasets/lijinghai/RabbitRobot-VLN-Recovery-Mini">Open VLN Dataset</a>
        <a class="button" href="https://huggingface.co/collections/lijinghai/rabbitrobot-vln-and-open-robot-assets-6a71a24e6d087ac0ff031ef5">Open Collection</a>
      </div>
    </div>
  </header>

  <main>
    <section class="band">
      <div class="wrap">
        <div class="section-head">
          <p class="kicker">Research Spine</p>
          <h2>VLN 不只是听懂指令，而是把语言目标落到真实机器人执行闭环里。</h2>
          <p class="lead">当前主线关注长程 VLN 中的偏航检测、目标歧义处理、语义子目标规划、任务记忆保持和可验证恢复。公开资产会先从小而干净的数据结构开始，逐步扩展到 rosbag replay、视觉 grounding 和真实 AMR 评估。</p>
        </div>
        <div class="pipeline">
          <div class="step"><b>Language Goal</b><span>自然语言目标、空间关系、语义子目标和任务阶段记忆。</span></div>
          <div class="step"><b>Grounding</b><span>把语言目标落到地图、视觉帧、waypoint 候选和可到达区域。</span></div>
          <div class="step"><b>Nav2 Execution</b><span>路径通过 ROS 2 / Nav2 / costmap / controller，而不是只画规划线。</span></div>
          <div class="step"><b>Deviation Detection</b><span>用里程计、costmap、局部规划状态和视觉信号发现偏航。</span></div>
          <div class="step"><b>Verifiable Recovery</b><span>恢复到安全、可解释、可继续执行的状态，并记录评估证据。</span></div>
        </div>
      </div>
    </section>

    <section class="band">
      <div class="wrap media-grid">
        <figure class="media">
          <img src="assets/amr2-web-navigation.jpg" alt="RabbitRobot AMR web navigation interface">
          <figcaption>RabbitRobot 的导航与 Web 工具链：真实机器人状态、地图和任务入口需要能被观察、复盘和继续开发。</figcaption>
        </figure>
        <figure class="media">
          <img src="assets/coverage-generated-path.png" alt="RabbitRobot coverage path planning overlay">
          <figcaption>覆盖路径规划是 VLN 执行侧的工程支撑：区域选择、known-free 裁剪、车体安全膨胀和 waypoint 生成。</figcaption>
        </figure>
      </div>
    </section>

    <section class="band">
      <div class="wrap">
        <div class="section-head">
          <p class="kicker">Open Assets</p>
          <h2>当前公开资产按 VLN 主线组织。</h2>
          <p class="lead">先发布可公开、可解释、可继续扩展的小资产；真实大数据会在脱敏、切片和文档完善后逐步进入。</p>
        </div>
        <div class="asset-grid">
          <a class="asset" href="https://huggingface.co/datasets/lijinghai/RabbitRobot-VLN-Recovery-Mini">
            <strong>RabbitRobot VLN Recovery Mini</strong>
            <p>长程 VLN/VLR task cards：自然语言指令、偏航事件、可观测信号、恢复策略和成功标准。</p>
            <div class="tag-row"><span class="tag">VLN</span><span class="tag">Recovery</span><span class="tag">Dataset</span></div>
          </a>
          <a class="asset" href="https://huggingface.co/spaces/lijinghai/RabbitRobot-CoveragePath-Demo">
            <strong>RabbitRobot Coverage Path Planning</strong>
            <p>静态交互演示：地图约束、footprint clearance、known-free clipping 与覆盖 waypoint 生成。</p>
            <div class="tag-row"><span class="tag">Nav2</span><span class="tag">Planning</span><span class="tag">Space</span></div>
          </a>
          <a class="asset" href="https://huggingface.co/datasets/lijinghai/R550-ROS2-Graph-Dictionary">
            <strong>R550 ROS2 Graph Dictionary</strong>
            <p>真实机器人 ROS 2 图谱字典：节点、Topic、Service、命令和功能域关系，服务系统理解与调试。</p>
            <div class="tag-row"><span class="tag">ROS 2</span><span class="tag">Graph</span><span class="tag">Dataset</span></div>
          </a>
        </div>
      </div>
    </section>

    <section class="band dark-band">
      <div class="wrap">
        <div class="section-head">
          <p class="kicker">Roadmap</p>
          <h2>从真实机器人底座走向可复现 VLN 评估。</h2>
          <p class="lead">这条线的价值不是单次演示，而是让机器人系统、实验数据和评估协议逐步变成别人可以检查、复现和继续研究的开放资产。</p>
        </div>
        <div class="roadmap">
          <div><b>1. AMR Baseline</b>ROS 2 / Nav2 / mapping / sensor integration.</div>
          <div><b>2. System Graph</b>ROS 2 nodes, topics, services and launch flows.</div>
          <div><b>3. Planning Demo</b>Coverage path and safe execution-chain thinking.</div>
          <div><b>4. VLN Cards</b>Instruction grounding, deviation events and recovery labels.</div>
          <div><b>5. Replay</b>Filtered rosbag slices, visual grounding and failure traces.</div>
          <div><b>6. Evaluation</b>Success, deviation, recovery and explainability metrics.</div>
        </div>
      </div>
    </section>

    <section class="band closing">
      <div>
        <h2>面向猎头、导师和合作者</h2>
        <p>如果你想快速判断我的方向：我是把真实机器人系统、ROS 2 工程和 VLN/VLR 研究问题放在同一条线上做的人。更完整的项目经历在个人主页和 GitHub。</p>
      </div>
      <a class="button" href="https://lijinghai.github.io/">Open Personal Site</a>
    </section>
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