Sim-to-real benchmark design: what to measure when hardware is scarce
Benchmark design should make the evaluation protocol shareable and reproducible. Pass rates without failure modes are not diligence.
Practical guides on verification, benchmarking, and reproducible evaluation for physical AI — written for builders who need claim boundaries, not demo reels.
Need a private eval? Request one or subscribe via RSS.
Benchmark design should make the evaluation protocol shareable and reproducible. Pass rates without failure modes are not diligence.
Physical-AI teams often blend sim success rates, world-model FID-style scores, and a manipulation demo into one headline. Independent evaluation separates them.
Navigation stacks need evaluation that stresses localization drift, sensor dropout, and obstacle behavior — not only clean-lab success rates.
A simulator can be excellent for training and weak for release evaluation unless you apply an independent physics-consistency check with reproducible seeds.
A successful demo does not prove a policy survives object mass changes, contact noise, or friction resampling. Here is how to verify pick-and-place claims.
A new dexterous-hand demo goes viral every few months. Without reproducible grasp protocols and failure taxonomies, progress stays unmeasurable.
If a world model looks realistic in a few clips, that is evidence — not proof. Evaluation turns visual plausibility into a reproducible, bounded claim.
Higher simulator fidelity does not automatically create trust. Independent evaluation of physics assumptions — with shown failures — is what changes release decisions.
A fixed Physics-IQ verification protocol on a held-out generative cohort: zero flags on real quiet video, 100% flags on CogVideoX via static_hover — and what that does not prove.