BENCHMARKS

Benchmark physical AI by the failures that matter

A benchmark is useful when another team can understand the mission, assumptions, seeds, outcomes and limits behind the result.

Research

Case studies

Mission completion is not enough

Track collisions, blocked missions, guardrail interventions, deviation, clearance and the conditions that caused non-completion.

Repeatability by design

Use seeded PhysX and Monte Carlo trials so a result can be reproduced, compared across platforms and inspected instead of treated as a single anecdote.

A benchmark package teams can review

Export trial data, charts, route evidence and assumptions so evaluation can support engineering decisions and future validation.