Behavior-driven robotics evaluation
A useful robot evaluation starts with a precise behavior contract, then keeps the task definition connected to the exact configuration and evidence produced by execution.
1. Define the behavior before the run
Start with the task objective, constraints, observations, action sequence and success criteria. A structured behavior gives the evaluation a stable object to reference instead of relying on an ad-hoc script.
2. Version the task contract
Behavior specifications change. Store an explicit version so a policy regression can be separated from a change in the task itself. This also makes historical experiment records easier to interpret.
3. Control the experiment
Keep the engine, policy identifier, seeds and evaluation settings explicit. The goal is not to remove variability, but to make the sources of variability visible and repeatable.
4. Separate validation from measurement
A configuration can be syntactically valid without producing a successful physical outcome. Treat schema or deterministic checks as validation, then report measured simulation results as a separate stage.
5. Preserve the evidence
An experiment is more useful when its measured output stays attached to the behavior version and execution configuration that produced it. That record supports comparison, debugging and later replay.
A compact evaluation loop
Behavior → Version → Experiment → Simulation → Measured Result → Replay → Report
Related resources
Humanoid robot behavior · Robot behavior evaluation · Embodied AI evaluation · Robot policy evaluation