physicalai-runtime-working-with-config

# Working with `physicalai.config` Runtime owns `src/physicalai/config/`. Physical AI Studio should import this package from `physicalai`, not copy it under `library/`. ## Workflow 1. **Pick the API** - Portable YAML recipes (robots, cameras, exported components): `Config` and `@export_config`. - Known Python types (trainers, dataclass configs, CLI models): jsonargparse (`ArgumentParser`, `add_class_arguments`, `parse_object`, `instantiate`). 2. **Author a recipe** — `class_path` + `init_args`; nest recipes only for trusted local config. See `docs/how-to/config/instantiate-components.md`. - Done when: dict/YAML passes validation without `ConfigError`. 3. **Export live objects** — `@export_config`, then `Config.from_instance(obj)` and `Config.save()`. Only trusted local sources. - Done when: saved YAML reloads with `instantiate()` in tests. 4. **Typed construction** — use jsonargparse in the owning package (runtime CLI, inference, and so on). Avoid new generic loaders under `physicalai.config`. 5. **Document and test** — update `docs/explanation/configuration.md` or `docs/how-to/config/`; extend `tests/unit/config/`. ## Validation loop ```bash uv run pytest tests/unit/config/ -q prek run ruff-check --all-files ``` ## Required checks - Deeply nested config hits `_MAX_CONFIG_DEPTH` and raises `ConfigError`. - `class_path` comes only from trusted local config (`docs/development/security.md`). - Studio imports `Config` from runtime, not a duplicate tree in the library. ## References - `docs/explanation/configuration.md` - `docs/how-to/config/instantiate-components.md` - `tests/unit/config/`