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  <!-- Header -->
  <div class="header">
    <img src="/spaces/OpenRAL/README/resolve/main/openral-logo.svg" alt="OpenRAL logo" />
    <div class="header-text">
      <h1>OpenRAL</h1>
      <p>The runtime layer between VLA models and real robots. Open-source, typed, layered — composable like LangGraph, distributable like Hugging Face.</p>
    </div>
  </div>

  <!-- Badges -->
  <div class="badges">
    <a class="badge" href="https://github.com/AdrianLlopart/openral">⬛ GitHub</a>
    <a class="badge" href="https://docs.openral.dev">📄 Docs</a>
    <a class="badge" href="https://discord.gg/ZdNyUT4V5">💬 Discord</a>
    <span class="badge">Apache-2.0</span>
    <span class="badge">ROS 2 Jazzy</span>
    <span class="badge">Python 3.12</span>
    <span class="badge">16 robots · 20 rSkills · 22 benchmarks</span>
  </div>

  <!-- What is it -->
  <div class="pitch">
    <p>A VLA model alone can't run on your robot. It needs a camera pipeline, an observation normaliser, an action de-normaliser, a safety wrapper, a replanning layer when it fails, and a way to log everything for later fine-tuning. <strong>OpenRAL is that infrastructure.</strong></p>
    <br/>
    <ul>
      <li><strong>Typed runtime</strong> — eight well-defined layers connected by Pydantic v2 contracts. No magic globals, no hidden retries.</li>
      <li><strong>rSkill format</strong> — Hub repos containing weights, a <code>rskill.yaml</code> manifest, quantisation hints, latency budgets, and reproducible <code>eval/</code>. Install like a model.</li>
      <li><strong>Dual-system planning</strong> — a fast visuomotor policy (S1, 30–200 Hz) and a slow LLM planner (S2) emitting typed tool-calls. Replanning is bounded and explicit.</li>
      <li><strong>Safety kernel</strong> — deny-by-default, Python proposes, C++ disposes. <code>ROSSafetyViolation</code> is never silently caught.</li>
    </ul>
  </div>

  <!-- Quick start -->
  <h2>Get started</h2>
  <pre><code># Install (no clone needed)
curl -fsSL https://raw.githubusercontent.com/AdrianLlopart/openral/master/scripts/install.sh | bash
openral doctor

# Browse and install a skill
openral rskill list
openral rskill install OpenRAL/rskill-smolvla-libero

# Run a simulated rollout
just sim-libero

# Deploy to hardware
openral deploy run --config deployments/so100_pickplace.yaml</code></pre>

  <!-- Architecture -->
  <h2>Architecture</h2>
  <div class="arch">
    <div class="arch-row">
      <span class="arch-num">0</span>
      <span class="arch-name">HAL</span>
      <span class="arch-desc">Per-robot adapters — uniform connect / read_state / write_command Protocol</span>
      <span class="arch-tag tag-green">shipped</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">1</span>
      <span class="arch-name">Sensors</span>
      <span class="arch-desc">SensorSpec → ROS 2 streams (RGB-D, F/T, IMU, lidar, tactile)</span>
      <span class="arch-tag tag-green">shipped</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">2</span>
      <span class="arch-name">World State</span>
      <span class="arch-desc">tf2-aware 30 Hz snapshot; carries lifted detected_objects</span>
      <span class="arch-tag tag-green">shipped</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">3</span>
      <span class="arch-name">rSkill (S1)</span>
      <span class="arch-desc">Fast visuomotor policy — VLA, 30–200 Hz, async action chunks</span>
      <span class="arch-tag tag-green">shipped</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">4</span>
      <span class="arch-name">Reasoning (S2)</span>
      <span class="arch-desc">Slow LLM planner emitting typed ReasonerToolCall tool-calls</span>
      <span class="arch-tag tag-yellow">partial</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">5</span>
      <span class="arch-name">WAM</span>
      <span class="arch-desc">World Action Model — mental simulation, failure anticipation</span>
      <span class="arch-tag tag-gray">planned</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">6</span>
      <span class="arch-name">Safety</span>
      <span class="arch-desc">C++ separate process, deny-by-default, certifiable, E-stop on fault</span>
      <span class="arch-tag tag-yellow">partial</span>
    </div>
    <div class="arch-row">
      <span class="arch-num">7</span>
      <span class="arch-name">Observability</span>
      <span class="arch-desc">OpenTelemetry spans + LeRobotDataset v3 flywheel</span>
      <span class="arch-tag tag-green">shipped</span>
    </div>
  </div>

  <!-- What's in this org -->
  <h2>What's in this organization</h2>
  <div class="table-wrap">
    <table>
      <thead><tr><th>Type</th><th>What</th><th>Count</th></tr></thead>
      <tbody>
        <tr><td><code>rskill-*</code> models</td><td>Policy rSkills — VLA weights + manifest + eval</td><td>18</td></tr>
        <tr><td><code>rskill-*</code> models</td><td>Perception rSkills — RT-DETR object-detection</td><td>2</td></tr>
        <tr><td><code>dataset-*</code></td><td>LeRobotDataset v3 demonstration datasets</td><td>growing</td></tr>
      </tbody>
    </table>
  </div>

  <!-- rSkills -->
  <h2>Policy rSkills</h2>
  <p>Each rSkill is a self-contained Hub repo: <code>rskill.yaml</code> manifest, <code>model.safetensors</code> weights, <code>eval/</code> results, and a model card with runnable examples. Install with <code>openral rskill install OpenRAL/rskill-&lt;name&gt;</code>.</p>

  <div class="table-wrap">
    <table>
      <thead><tr><th>rSkill</th><th>Backbone</th><th>Target robot</th><th>License</th></tr></thead>
      <tbody>
        <tr>
          <td><code>rskill-smolvla-libero</code></td>
          <td>SmolVLA</td><td>Franka Panda</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-smolvla-metaworld</code></td>
          <td>SmolVLA</td><td>Rethink Sawyer</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-smolvla-maniskill-franka</code></td>
          <td>SmolVLA × ManiSkill3</td><td>Franka Panda</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-xvla-libero</code></td>
          <td>xVLA (Florence-2)</td><td>Franka Panda</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-act-libero</code></td>
          <td>ACT</td><td>Franka Panda</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-act-aloha</code></td>
          <td>ACT</td><td>ALOHA bimanual</td>
          <td><span class="lic lic-green"></span>MIT</td>
        </tr>
        <tr>
          <td><code>rskill-act-aloha-insertion</code></td>
          <td>ACT (peg insertion)</td><td>ALOHA bimanual</td>
          <td><span class="lic lic-green"></span>MIT</td>
        </tr>
        <tr>
          <td><code>rskill-diffusion-pusht</code></td>
          <td>Diffusion Policy</td><td>PushT 2-D</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-molmoact2-libero-nf4</code></td>
          <td>MolmoAct2 NF4 (~5.5 B)</td><td>Franka Panda</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-molmoact2-so101-nf4</code></td>
          <td>MolmoAct2 NF4</td><td>SO-101</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-pi05-libero-nf4</code></td>
          <td>π0.5 NF4</td><td>Franka Panda</td>
          <td><span class="lic lic-yellow"></span>Research (weights non-Apache)</td>
        </tr>
        <tr>
          <td><code>rskill-pi05-so101-pickplace-nf4</code></td>
          <td>π0.5 NF4</td><td>SO-101</td>
          <td><span class="lic lic-yellow"></span>Research (weights)</td>
        </tr>
        <tr>
          <td><code>rskill-pi05-openarm-vision-nf4</code></td>
          <td>π0.5 NF4 bimanual</td><td>OpenArm v2</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-pi05-robocasa365-human300-nf4</code></td>
          <td>π0.5 NF4</td><td>Panda Mobile</td>
          <td><span class="lic lic-yellow"></span>Research (weights)</td>
        </tr>
        <tr>
          <td><code>rskill-rldx1-pt-nf4</code></td>
          <td>RLDX-1 foundation (Qwen3-VL-8B, ~6.9 B)</td><td>Franka Panda</td>
          <td><span class="lic lic-red"></span>RLWRLD non-commercial</td>
        </tr>
        <tr>
          <td><code>rskill-rldx1-ft-libero-nf4</code></td>
          <td>RLDX-1 fine-tuned</td><td>Franka Panda</td>
          <td><span class="lic lic-red"></span>RLWRLD non-commercial</td>
        </tr>
        <tr>
          <td><code>rskill-rldx1-ft-gr1-nf4</code></td>
          <td>RLDX-1 (GR1 bimanual)</td><td>Fourier GR1</td>
          <td><span class="lic lic-red"></span>RLWRLD non-commercial</td>
        </tr>
        <tr>
          <td><code>rskill-rldx1-ft-simpler-widowx-nf4</code></td>
          <td>RLDX-1 (SimplerEnv)</td><td>WidowX</td>
          <td><span class="lic lic-red"></span>RLWRLD non-commercial</td>
        </tr>
      </tbody>
    </table>
  </div>

  <h2>Perception rSkills</h2>
  <p>Emit <code>ObjectsMetadata</code> (2D detections lifted to 3D via depth) rather than an <code>Action</code>. Plug directly into the <code>openral deploy</code> graph.</p>
  <div class="table-wrap">
    <table>
      <thead><tr><th>rSkill</th><th>Backbone</th><th>Notes</th><th>License</th></tr></thead>
      <tbody>
        <tr>
          <td><code>rskill-rtdetr-coco-r18</code></td>
          <td>RT-DETR R18</td><td>Lightweight ONNX</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
        <tr>
          <td><code>rskill-rtdetr-v2-r50vd</code></td>
          <td>RT-DETR v2 R50vd</td><td>Higher accuracy</td>
          <td><span class="lic lic-green"></span>Apache-2.0</td>
        </tr>
      </tbody>
    </table>
  </div>

  <!-- License legend -->
  <p style="font-size:12.5px; color: var(--mid);">
    <span class="lic lic-green"></span> Free for any use &nbsp;
    <span class="lic lic-yellow"></span> Research-permissive weights (check upstream terms) &nbsp;
    <span class="lic lic-red"></span> Non-commercial — requires <code>OPENRAL_ACCEPT_NONCOMMERCIAL=1</code>
  </p>

  <!-- rSkill manifest format -->
  <h2>rSkill manifest format</h2>
  <pre><code># rskills/smolvla-libero/rskill.yaml (excerpt)
name: "OpenRAL/rskill-smolvla-libero"
version: "0.1.0"
license: "apache-2.0"
role: "s1"
embodiment_tags: ["franka_panda"]
sensors_required:
  - modality: "rgb"
    vla_feature_key: "observation.images.camera1"
latency_budget_ms: 120
quantization: null</code></pre>

  <!-- Robots -->
  <h2>Supported robots</h2>

  <div class="robot-group">
    <h3>Hardware + sim</h3>
    <div class="robot-chips">
      <span class="chip chip-green">SO-100</span>
      <span class="chip chip-green">SO-101</span>
      <span class="chip chip-green">ALOHA bimanual</span>
    </div>
  </div>

  <div class="robot-group">
    <h3>Sim — HW bring-up in progress</h3>
    <div class="robot-chips">
      <span class="chip chip-blue">Franka Panda</span>
      <span class="chip chip-blue">UR5e</span>
      <span class="chip chip-blue">UR10e</span>
    </div>
  </div>

  <div class="robot-group">
    <h3>Sim (eval only)</h3>
    <div class="robot-chips">
      <span class="chip chip-gray">Unitree H1</span>
      <span class="chip chip-gray">Unitree G1</span>
      <span class="chip chip-gray">Flexiv Rizon 4</span>
      <span class="chip chip-gray">Enactic OpenArm v2</span>
      <span class="chip chip-gray">Rethink Sawyer</span>
      <span class="chip chip-gray">Fourier GR1</span>
      <span class="chip chip-gray">Panda Mobile</span>
      <span class="chip chip-gray">Google Robot</span>
      <span class="chip chip-gray">WidowX</span>
      <span class="chip chip-gray">PushT 2D</span>
    </div>
  </div>

  <!-- Benchmarks -->
  <h2>Benchmarks</h2>
  <p>22 benchmark configs across LIBERO, MetaWorld, ManiSkill3, SimplerEnv, RoboCasa, gym-aloha, and gym-pusht. Every rSkill ships reproducible <code>eval/&lt;benchmark&gt;.json</code> you can regenerate locally:</p>
  <pre><code>openral benchmark run \
  --suite libero_spatial \
  --vla smolvla:rskill://OpenRAL/rskill-smolvla-libero

openral benchmark report</code></pre>

  <!-- Observability -->
  <h2>Observability &amp; data flywheel</h2>
  <p>Every skill execution is an OpenTelemetry span — weights revision pinned, camera frames captured, LLM prompts logged. Traces replay as LeRobotDataset v3 rows, closing the loop from deployment back to training data.</p>
  <pre><code>openral dashboard   # OTLP receiver at :4318, live trace viewer</code></pre>

  <!-- License -->
  <h2>License</h2>
  <p>Everything in this organization is <strong>Apache-2.0</strong> open core. A planned commercial tier (PolyForm SBL 1.0.0) will cover agentic orchestration features — free for teams under 100 people or &lt;$1M revenue, and free for academic research at any size.</p>
  <p>rSkill weights are governed by their upstream licenses (Apache-2.0 / MIT / research-permissive / RLWRLD non-commercial). The loader surfaces the posture at install time.</p>

  <!-- Footer -->
  <div class="footer">
    <a href="https://github.com/AdrianLlopart/openral">GitHub</a>
    <a href="https://docs.openral.dev">Docs</a>
    <a href="https://discord.gg/ZdNyUT4V5">Discord</a>
    <a href="mailto:hello@openral.dev">hello@openral.dev</a>
    <a href="mailto:safety@openral.dev">safety@openral.dev</a>
    <span>Apache-2.0 · © OpenRAL contributors</span>
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