physicalai-train-training-a-policy

physicalai-train-training-a-policy is a Model training & evaluation skill for AI agents, published by open-edge-platform in the physical-ai-studio repository on GitHub. Skillspace rates physicalai-train-training-a-policy C (74/100) and it passed the safety scan; the rating is based on static analysis only.

Trains, validates, tests, and runs prediction for Physical AI Studio policies via the library Lightning stack. Use when running physicalai fit/validate/test/predict, calling physicalai.train.Trainer and Policy APIs from Python, writing or editing YAML configs under library/configs, wiring a model + datamodule + trainer, resuming from a checkpoint, or debugging a training run. Covers ACT, Pi0, Pi0.5, GR00T, and SmolVLA.

Grade
C
Quality
74 / 100
Safety
Passed
GitHub stars
57

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubopen-edge-platform/physical-ai-studio

Our review

Architecture
100 / 100
Maturity
Basic: a single SKILL.md
Security scan
No issues found
License
Apache-2.0
Adoption
Adopted by 0 other repositories, in 0 collections

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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