Développement
Agent-Skills
Curated AI agent skills for Microsoft and Azure, sourced from Microsoft Learn.
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Voir le guide →physical-ai-toolchain est un projet de développement open source, créé par microsoft (microsoft/physical-ai-toolchain sur GitHub). Il compte 123 étoiles sur GitHub et 62 forks, il est publié sous la licence MIT et a été mis à jour pour la dernière fois sur GitHub le 8 octobre 2026.
Microsoft's Physical AI Toolchain: tiered pipelines from robot data capture to edge deployment.
Choose your tier honestly: start at T0 (one laptop, one robot, no cloud) unless your scale already demands more.
Clone the repository and run ./setup-dev.sh, which installs Python 3.12 via uv, creates a virtual environment, and installs training dependencies.
Capture demonstrations on the robot with ROS 2 bag recording and move the data to your laptop.
Curate the data, train an imitation policy with LeRobot tooling, validate it locally, and run the policy back on the robot.
Six tiers from a zero-cloud laptop setup to multi-site fleet delivery, each stating the minimum infrastructure it needs so teams adopt only what their scale demands.
Simulation and synthetic data, edge data capture, cloud data pipelines with ROS-to-LeRobot conversion, training infrastructure, evaluation, deployment packaging, and edge inference are all addressed.
Native support for Isaac Sim and Isaac Lab simulation, OSMO workflow orchestration, and Jetson edge inference.
Infrastructure as Code with Terraform, MLflow experiment tracking, Entra ID with managed identities, and GitHub Actions CI/CD enter at the tiers that need them.
Inference can be offloaded to an on-premise GPU through Kubernetes operators, saving robot battery and cost.
Optional agents can turn a high-level instruction into executed collection, training, evaluation, and deployment stages, within the same security boundaries as manual workflows.
Get the framework, tier recipes, and documentation locally.
git clone https://github.com/microsoft/physical-ai-toolchain.git
cd physical-ai-toolchainThe script installs Python 3.12 via uv, creates the virtual environment, and installs training dependencies.
./setup-dev.shWork through the Tier 0 Dev recipe in the docs: capture, curate, train, validate, and run locally. # see README Start on a Laptop section
# see the Tier 0 - Dev recipe linked from the READMEIt is maintained by Microsoft on GitHub.
Yes, the toolchain is MIT-licensed and its default T0 path needs no Azure subscription at all; cloud tiers use your own Azure account and incur normal Azure costs.
No key is needed for the local T0 path. Cloud tiers authenticate with your Azure and Entra ID setup, and agent features use your configured model access.
Standalone tools cover one stage, such as simulation or training; this toolchain is an integrated, tiered framework spanning capture through fleet deployment with production security and governance built in.
Clone the repo, run ./setup-dev.sh, and follow the Tier 0 Dev recipe to close the full loop on one laptop and one robot.
Curated AI agent skills for Microsoft and Azure, sourced from Microsoft Learn.
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