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| <div class="page"> |
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| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <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-<name></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> |
|
|
| |
| <p style="font-size:12.5px; color: var(--mid);"> |
| <span class="lic lic-green"></span> Free for any use |
| <span class="lic lic-yellow"></span> Research-permissive weights (check upstream terms) |
| <span class="lic lic-red"></span> Non-commercial — requires <code>OPENRAL_ACCEPT_NONCOMMERCIAL=1</code> |
| </p> |
|
|
| |
| <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> |
|
|
| |
| <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> |
|
|
| |
| <h2>Benchmarks</h2> |
| <p>22 benchmark configs across LIBERO, MetaWorld, ManiSkill3, SimplerEnv, RoboCasa, gym-aloha, and gym-pusht. Every rSkill ships reproducible <code>eval/<benchmark>.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> |
|
|
| |
| <h2>Observability & 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> |
|
|
| |
| <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 <$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> |
|
|
| |
| <div class="footer"> |
| <a href="https://github.com/AdrianLlopart/openral">GitHub</a> |
| <a href="https://docs.openral.dev">Docs</a> |
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