OpenRAL
The open-source Robot Agentic Layer (RAL), the harness for physical AI.
OpenRAL, is the typed, traceable, safety-first runtime that holds an embodied-AI stack together: fast policies, slow reasoning, reward signals, perception, and classical control — one contract over many robots, many models, and one safety boundary, on real hardware and in simulation.
The problem: every robot SDK, VLA, and sensor speaks its own dialect, so integration is one-off glue code. Safety usually runs inside the same process that can crash. Swapping a model is a rewrite. And runs aren’t reproducible.
What the harness gives you:
• One typed contract between every layer (Pydantic v2 schemas on ROS 2, tf2, MoveIt 2, Nav2, ros2_control).
• rSkills (robot skills) packaged like models (HF Hub repo: typed manifest, weights, reproducible eval). Kinds: VLA policies, perception detectors, scene VLMs, reward models, ROS actions. Hot-swappable.
• Fast + slow control — slow LLM reasoner dispatches skills via typed tool-calls; fast VLA policies at 30–200 Hz.
• Perception AI into a live world state — open-vocab detectors + scene VLM lift 2D→3D into a tf2-aware world state + spatial-memory scene graph.
• Sim → benchmark → real deployment, one contract — LIBERO, MetaWorld, ManiSkill3, SimplerEnv, RoboCasa, Isaac Sim → deploy on 16 embodiments.
• A C++ safety kernel — deny-by-default, screens action chunks against an Allowed Collision Matrix, deadman + E-stop.
• Observability and traceability: every run is an OpenTelemetry trace, replayable in dashboard + Foxglove, foldable into a LeRobot dataset.
Everything is Apache-2.0. Feedback, issues, and contributors very welcome.
Code: GitHub - OpenRAL/openral: Open-source Robot Agentic Layer · GitHub · Site: openral.com · Discord: discord.gg/3paXT2bVyB
