The Physical AI skills your agents need
An open-source Physical AI harness: a continuously evolving collection of field-tested robotics expertise spanning the leading frameworks, simulators, libraries, and developer tools. Install Robium as a plugin to empower your favorite AI coding agent with the robotics skills it needs.
npx robium-ai setup ✓ robium skills installed into Claude Code claude > scaffold a project: a LeRobot arm on a mobile ROS 2 base, LeRobot + Nav2, sim first ⚙ skills loaded: architect · lerobot · nav2 · simulation · environments > set up the VLA fine tuning pipeline on RunPod with dataset from hugging face ⚙ skills loaded: environments · data · huggingface > deploy the sim demo to Cloud Run ⚙ skills loaded: live-demo · cloud-run
Field-tested, distilled skills from the leading Physical AI platforms
Distilled from real projects
Every skill is distilled from production codebases, open-source projects, reference applications, working examples, and real engineering sessions across the Physical AI ecosystem.
Self-evolving
Robium continuously evolves by learning from the robotics ecosystem and your team's codebase, custom hardware, documentation, and engineering workflows.
Works across the ecosystem
Comprehensive support for the leading Physical AI frameworks, simulators, libraries, datasets, developer tools, and cloud platforms. All your robotics expertise in one place.
Works with your agent
Install Robium as a plugin to empower your favorite AI coding agent with the robotics expertise needed to make informed engineering decisions across architecture, implementation, testing, and deployment.
Focus on your application
Robium handles environment setup, simulator configuration, integrations, project scaffolding, and version compatibility so you can focus on your robot, application, and ML models.
Open by default
MIT licensed, community-driven, and continuously expanding to support new frameworks, tools, and engineering workflows across the Physical AI ecosystem.
Pick your agent
One command installs Robium for the coding agents on your machine. Pull the repo anytime to update.
npx robium-ai setup Auto-detects your agents; clones to ~/robium (one prompt).
Prefer to install manually? View clone commands
git clone https://github.com/robium-ai/robium ~/robium
mkdir -p ~/.agents/skills && ln -s ~/robium/skills/* ~/.agents/skills/ Or skip step two: inside the clone, agents discover the skills automatically.
Your app stays in its own repo; Robium lives beside it as the source of truth. Any tool using the open Agent Skills standard can read the same install.
Describe the robot, the task, the hardware. Architect returns a full stack decision (middleware, sim, data, training) plus a written brief, then routes every skill below.
Smoke tests · sim regression · policy eval · CI
Canonical worlds · models · fixtures
Working app → public web demo
Ship headless sims to Cloud Run
Gazebo vs Isaac · sensor fidelity · sim-to-real
SDF worlds · sensors · ros_gz bridge · headless
MJCF · IK · contact-rich manipulation
USD scenes · ROS 2 bridge · headless GPU
RL & imitation on Isaac Sim
Offline datasets · sim-generated · teleop
Record episodes · train ACT / diffusion / pi0 · teleop
Hub datasets · models · Spaces
rviz2 vs Foxglove vs Rerun: what to watch, when
Layouts · MCAP · remote viz
Timelines · rollouts · sensor streams
TF debugging · display fixes
Build workspaces · launch systems · topics & TF
Behavior trees · costmaps · localization
Module boundaries · messaging · compose
uv vs Docker · identical local & remote runs
Authoring workflow · quality bar · validator
The agentic development harness
Robium provides robotics expertise, your project provides context, and your AI coding agent orchestrates the entire Physical AI engineering workflow, from architecture and implementation to simulation, testing, and deployment. Every successful build contributes new learnings that continuously improve future skills.
Bridge the robotics–AI gap
Whether you're coming from robotics or AI, Robium fills the missing expertise so your AI coding agent can build complete Physical AI systems.
From robotics to Physical AI
You know ROS, middleware, and hardware. Robium fills the ML gap, from datasets and LeRobot to policy training and modern Physical AI workflows.
From AI to robotics
You know Python, PyTorch, and model training. Robium fills the robotics gap, from ROS 2 and simulation to deployment on real robots.
Standardize robotics development
Give every engineer the same robotics expertise while capturing your team's knowledge into reusable skills that improve over time.
What's inside Robium
robium · MITOne install adds the whole toolchain to your agent: the skills carry the expertise, an architect agent picks the stack, and the reference apps prove the whole thing runs.
robium/ ├── skills/ versioned expertise │ ├── architect │ ├── ros2 · nav2 · gazebo │ └── … ├── agents/ │ └── robium-architect ├── apps/ reference applications ├── cli/ npx robium-ai setup └── learnings/ the feedback loop
skills
The expertise. Versioned robotics skills containing field-tested engineering knowledge, ecosystem integrations, patterns, and best practices.
agents
The orchestrators. Specialized agents that analyze requirements, select the right stack, and coordinate the engineering workflow.
applications
The reference implementations. Complete robotics applications that validate the skills and serve as production-ready examples.
cli
The developer tools. Install Robium, validate your environment, discover skills, and keep everything up to date.
learnings
The feedback loop. Captured engineering learnings continuously improve the skill library through reviewed updates.
Full skill catalog
generated from the repo at build time · browse on GitHub →Every skill is versioned, field-tested by the reference apps, and open source. Search, filter by category, or expand a row for the full description, related skills, and source links.
architect v1.8.1 Turns requirements into a full stack decision and a written architecture brief. stack selectiondesign Architecture
Entry-point skill for designing robotics applications with AI agents. Turns requirements (robot type, task, hardware, sim-vs-real, GPU/budget) into a full stack decision (middleware, simulation, data, visualization, training frameworks) plus a scaffold plan and a written architecture brief.
gazebo v1.3.3 SDF worlds, sensors, the ros_gz bridge, and headless operation. GazeboSDFros_gz Simulation
Modern Gazebo (gz, Harmonic/Ionic line) simulation: SDF worlds and models, sensors (lidar, camera, IMU, contact), the ros_gz bridge, spawning robots, and headless/server operation.
isaac-lab v1.1.2 RL and imitation learning on Isaac Sim. Isaac SimRLimitation Simulation
NVIDIA Isaac Lab: reinforcement-learning and imitation-learning workflows on top of Isaac Sim: prebuilt environments and tasks, training runs, and exporting policies.
isaac-sim v1.1.2 GPU requirements, USD scenes, the ROS 2 bridge, headless operation. Isaac SimUSDGPU Simulation
NVIDIA Isaac Sim: installation and container setup, GPU/driver requirements, USD scenes, robots and sensors, the ROS 2 bridge, and headless/livestream operation for remote servers.
mujoco v1.0.2 Lightweight, contact-rich manipulation simulation without ROS. MuJoCoMJCFmenagerie Simulation
MuJoCo for lightweight, contact-rich robot manipulation simulation on macOS/Linux, especially single-arm grasping without ROS: headless offscreen rendering, MJCF models, mujoco_menagerie assets, damped-least-squares inverse kinematics, and empirical grasp calibration.
simulation v1.1.2 Choose the right simulator and simulate sensors correctly. GazeboIsaac Simsensors Simulation
Choose and set up robotics simulators, and simulate sensors correctly: Gazebo vs Isaac Sim selection, sensor fidelity (rates, noise models, frames matching the real robot), determinism, and sim-to-real considerations.
data v1.2.3 Choose between offline datasets, sim-generated data, and teleop collection. Open X-Embodimentteleop Data
Data sourcing strategy for robotics and physical-AI: choose between offline datasets (HuggingFace hub, Open X-Embodiment and similar), simulation-generated data, and teleop/real-robot collection; plan storage formats, episode structure, and dataset versioning.
huggingface v1.1.2 Hub datasets, models, demo Spaces. Hugging FaceHubSpaces Data
HuggingFace ecosystem for robotics projects: hub datasets and models for robot learning, and demo Spaces. DELEGATES: for hub mechanics (download/upload/auth/jobs), install HuggingFace's own skills (/plugin marketplace add huggingface/skills, then /plugin install hf-cli@huggingface-skills) and defer to them; this skill adds only the robotics-specific layer (which datasets and models matter for manipulation and navigation, robotics dataset conventions on the hub).
lerobot v2.1.3 Datasets, policy training, evaluation, and teleoperation. LeRobotACTSmolVLA Data
HuggingFace LeRobot for physical-AI manipulation: the LeRobotDataset format, loading and recording episodes, training policies (ACT, diffusion, pi0) and VLAs (SmolVLA), evaluating in simulation, and teleoperation.
foxglove v1.5.2 Layouts, MCAP, remote visualization. Foxglovefoxglove_bridgeMCAP Visualization
Foxglove for robotics visualization: foxglove_bridge setup for live ROS 2 robots, layouts, MCAP recording and playback, and remote/web visualization of robots running on servers.
rerun v1.2.2 Log and view rollouts and streams. ReruntimelinesPython API Visualization
Rerun for data-centric robotics and ML visualization: logging APIs (Python), timelines, entity paths, and viewing policy rollouts, episode data, and sensor streams.
rviz2 v1.0.3 TF debugging and display fixes. RViz2TFmarkers Visualization
RViz2 visualization for ROS 2: displays, TF frame debugging, markers, saved config files, and the common 'nothing shows up' fixes (fixed frame, QoS, sim time).
visualization v1.0.4 Choose rviz2 vs Foxglove vs Rerun, and what to visualize when. rviz2 vs Foxglove vs Rerun Visualization
Choose and apply robotics visualization: selection guidance for rviz2 vs Foxglove vs Rerun, plus best practices: what to visualize at each dev stage, live vs recorded, local vs remote.
environments v1.7.1 Decide uv vs Docker and make local and remote runs reproduce identically. uvDockerGPU Infrastructure
Virtual-environment-first setup for robotics projects: decide uv/venv vs Docker, make local and remote-server runs reproduce identically, handle GPU passthrough and headless/display forwarding.
integration v1.2.3 Module boundaries, inter-module communication, and compose wiring. ROS 2zenohgRPC Infrastructure
Glue robotics modules into one running system: choose module boundaries, pick inter-module communication (ROS 2 topics/services/actions, zenoh, gRPC, REST, shared memory), and write solid Dockerfiles and docker-compose for robotics workloads.
nav2 v1.5.1 Behavior trees, costmaps, and tuning. Nav2behavior treesAMCL Infrastructure
Nav2 mobile-robot navigation for ROS 2: bringup, behavior trees, costmaps, planner/controller servers, localization (AMCL, slam_toolbox), waypoint following, and tuning.
ros2 v1.7.1 Workspaces, colcon, topics, QoS, TF2. ROS 2colconQoS Infrastructure
Core ROS 2 usage: workspaces, colcon builds, packages (ament_python/ament_cmake), nodes, topics/services/actions, QoS, launch files, parameters, TF2, rosdep, and gluing third-party packages together.
cloud-run v1.0.2 Deploy sim containers to Cloud Run. Cloud RunArtifact RegistryWebSockets Deployment
Deploy headless robotics / sim / demo containers to Google Cloud Run: the build → Artifact Registry → Cloud Run path plus the gotchas that bite sim workloads (no UDP multicast for gz-transport/DDS, CPU allocated only while a request is open, session affinity for per-visitor instances, request timeout / concurrency for long-lived WebSockets, VPC subnet sizing).
live-demo v1.3.3 Turn a working app into a public, interactive web demo. Cloud RunFoxglove Deployment
Turn a working robium app into a public, interactive web demo: a mission-control demo page (start/stop instance buttons, live boot terminal, fleet budget), per-visitor simulator instances on Cloud Run (scale-to-zero), and a visualizer handoff (Foxglove deep link or self-hosted viewer).
learning-loop v0.2.1 Consolidates captured learnings into evidence-gated skill-update PRs. consolidateabsorbPRs Meta
The session-side surface of robium's learning engine: consolidate captured flags and learnings into evidence-counted observations, absorb ready observations into anchor-targeted skill-edit PRs via the deterministic delta pipeline, refine (prune/dedup/staleness) through the same pipeline, run blind variant A/B on contested edits ('experiment', 'A/B this edit', 'try competing fixes'), deep-verify unverified examples in pinned fixtures ('deep verify', 'verify the examples'), and report loop health.
mining v0.1.2 Mines external repos for the catalog. external reposobservations Meta
Registry-driven mining of external example repos: the learning engine's second experience source. Surveys, deep-reads, and comparatively analyzes approved repos (vendor demos, framework samples, community robot apps) into evidence-cited observations (origin: external) that harden robium skills or propose new ones; maintains crawl records for drift re-checks.
skill-author v2.0.2 Authoring workflow and the quality bar. templatequality bar Meta
Author new robium skills and enforce the catalog quality bar. Owns the authoring workflow from skills/_TEMPLATE, the quality bar (references/quality-bar.md: template compliance, trigger-surface descriptions, <500-line bodies, stated delegation posture, upstream links, no invented syntax), and scripts/validate_skills.py: run it after ANY skills/ change.
test-assets v1.0.3 Canonical worlds, models, and datasets. worldsfixturesgolden policy Meta
Canonical test assets and fixture sourcing for robotics testing: which worlds, robot models, sample datasets, and recordings to test against for a given robot type; the standard test-assets folder layout with a provenance manifest; pointer vs vendored sourcing modes; fixture and golden policy (tolerance bands, seeded generation).
testing v1.4.3 Smoke tests, sim-based regression, policy eval, and CI patterns. launch testsCI Meta
Test-driven robotics development: smoke tests for launch files, sim-based regression tests, node-level unit tests, policy eval as a test, and CI patterns for robotics repos.
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Frequently asked questions
Which coding agents does Robium work with?
Claude Code, Codex, Gemini CLI, and Cursor. One command,
npx robium-ai setup, detects the agents on your machine
and sets up each one (or target one: --agent codex). The
skills follow the open Agent Skills format, so any agent that speaks
it can read them.
Do I need a robot or a GPU?
Not necessarily. Everything starts in simulation: manip-trial trains and evaluates a policy on a GPU-less MacBook, and nav-trial runs Gazebo fully headless in Docker. When a build does need muscle, the skills cover running the same stack on remote GPU servers and in the cloud.
What exactly is a skill?
A versioned folder of expertise your agent loads when a task calls for it: field-tested guidance (which simulator, which viewer, the failure modes docs don't mention) plus curated reference notes and runnable examples: real Dockerfiles, launch files, SDF worlds, and Python snippets. No invented syntax to learn; just knowledge your agent acts on.
Why not just ask my agent directly?
Frontier agents know robotics in general. They don't reliably know which Gazebo pairs with which ROS 2 release, or why a cloud-hosted sim needs a unicast relay. Skills pin that judgment and those facts, versioned and verified against real builds, so your agent doesn't re-derive or hallucinate them.
How do skills stay correct as the ecosystem moves?
The catalog runs a learning engine: build sessions capture what broke and what fixed it, mining pulls proven patterns from ecosystem repos, and evidence-gated pull requests fold both back into the versioned skills; a human merges every change. LeRobot's API churn has already forced a major version bump.
Can it capture my team's own knowledge?
Yes. Capture hooks log what breaks and what works during your builds, and the skill-author workflow turns that into skills for your own stack (your conventions, your hardware, your infra), hardened by the same loop that maintains the public catalog.
Is it free?
Yes, MIT-licensed. The plugin, the full skill catalog, and the CLI are open source at github.com/robium-ai.
Can I contribute a skill?
Yes, and the contribution unit is deliberately small: one skill. Copy the template, pass the validator, open a PR.