Sim-To-Real

When To Use

Use this skill for robotics teams that need configurable data and artifact flow through simulation, policy training, synthetic data generation, evaluation, and iteration without customer-specific names or infrastructure baked into source.

Procedure

1. Define a run ID and run-scoped S3 prefixes before launching.
2. Import source robot, scene, or task data into the run prefix.
3. Generate or augment simulation data with configured workbench tools.
4. Train or fine-tune the policy against the run-scoped dataset and write checkpoints to S3.
5. Evaluate the policy with deterministic metrics or a configured VLM backend.
6. Decide whether to stop, continue, or route artifacts for review based on configurable thresholds.

Three-Tier Contract

• CLI: use npa workbench workflow, npa workbench trigger, and tool commands such as Genesis, LeRobot, SONIC, MJLab, Retargeting, LanceDB, Cosmos, and VLM-eval.
• SDK: keep workflow submission and config materialization in shared helpers so notebooks and services use the same artifact paths.
• Workflows: workflows/testing/vlm-eval-loop.yaml is the executable reference for the VLM-eval gating loop (scores a whole rollout set and writes task_success_report.json). The staged engine's single maintained end-to-end YAML is workflows/main/sim2real.yaml; similarly named npa.workflow files are explicitly demo-only DSL fixtures. sim-to-real-pipeline.yaml and sim-to-real-trigger.yaml are retired — the first ran npa.workflows.sim_to_real real-loop, which raises a DeprecationWarning pointing here.

Gotchas

• Do not hardcode customer names, event names, personal names, tenant IDs, registry IDs, bucket names, VM IPs, or private endpoints.
• Prefer dry-run plans for data movement, autoscaling, and external service calls when validating workflow shape.
• Keep artifacts partitioned by run ID so repeated experiments do not overwrite each other.
• Use existing workbench tools for S3 sync, model inference, training, and VLM evaluation instead of one-off scripts.

Verify

npa/.venv/bin/python -m pytest npa/tests/guardrails/test_skills_index.py -q

The smoke test parses the sim-to-real YAMLs and confirms the workflow CLI help loads through the installed package.