openvinotoolkit/physicalai/pai-load-policy

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Loads and validates policies exported from Physical AI Studio for Runtime deployment. Use when calling InferenceModel or from_pretrained on a local export or Hub snapshot, checking manifest.json, selecting an ONNX/OpenVINO backend or device, or smoke-testing an action. DO NOT USE FOR implementing an inference adapter in Runtime source.

Install

npx vskill@latest install openvinotoolkit/physicalai/pai-load-policy

SKILL.md

Loading Exported Policies

Runtime loads Studio export directories (or Hub snapshots that mirror them) through InferenceModel. It reads manifest.json, auto-detects registered ONNX/OpenVINO adapters from model files, and supports Hub revisions. See the inference API and manifest schema.

Workflow

  1. Identify the artifact: local export directory or Hub repo_id, expected backend, and whether the user needs select_action vs predict_action_chunk.
  • Done when: load path and API entry point are chosen before editing code.
  1. Load with auto-detection first (local):

``python from physicalai.inference import InferenceModel model = InferenceModel("./exports/act_policy") ``

  • Done when: model constructs without explicit backend= when artifacts match a registered extension.
  1. Hub load when the package is published:

``python model = InferenceModel.from_pretrained("OpenVINO/act-fp16-ov", revision="<commit-sha>") ``

  • Done when: revision is pinned for reproducibility when security or CI matters.
  1. Explicit backend only when auto-detection is ambiguous:

``python model = InferenceModel("./exports/act_policy", backend="openvino", device="CPU") ``

  1. Validate structure against the manifest schema and backend notes (references/onnx.md, references/openvino.md).
  • Done when: manifest, model file, and processor artifacts resolve under the export directory.
  1. Smoke inference without owning robot timing:

``python model.reset() action = model.select_action(observation) ``

  • Done when: one forward pass succeeds on representative observation keys/shapes. For hardware loops, hand off to pai-run-policy.

Validation loop

Construct InferenceModel from the export and run one representative fake observation without hardware. If a backend cannot be loaded, check its installed optional dependency and the documented adapter support.

Required checks

  • Export directory contains backend model file(s) and manifest.json consistent with docs/reference/manifest-schema.md.
  • Metadata input/output/feature names align with preprocessors in the manifest.
  • Optional backends fail with clear install guidance (onnxruntime, OpenVINO).
  • Do not claim support for an adapter absent from the installed Runtime package.
  • Hub loads must not log tokens; prefer pinned revision= for production docs.

References

  • Manifest schema — canonical export/load contract.
  • references/onnx.md, references/openvino.md — adapter constraints.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

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