Robot policy evaluation
A policy evaluation becomes more useful when the task, version, seed and measured outcome remain connected.
Policy evaluation workflow
Define the behavior and success criteria. Select the policy and version. Run the supported simulation configuration with explicit seeds. Persist the result and inspect the report.
What to record
Useful experiment records include the behavior identifier, behavior version, policy identifier, engine, seeds, execution status and measured metrics. Keeping these fields together supports later comparison.
Benchmarking policy changes
When the evaluation setup stays stable, teams can compare policy iterations with less ambiguity. This is especially useful for debugging regressions and tracking progress through repeated experiments.
Related robotics evaluation resources
HumanoidBehavior home · Behavior library · Benchmarking · Developer docs · Technical demo