AnyRank ranks robot policies reliably with far fewer real-robot trials
pappasg69 · x · 2026-10-27
A CORL 2026 paper (arXiv:2610.14603) introduces AnyRank, a sequential-testing method for ranking multiple robot policies despite costly hardware trials. It adaptively allocates trials to hard-to-separate policies, delivers anytime-valid partial rankings with bounded error probability, and exploits possibly imperfect priors from expert intuition, held-out evaluations, simulators, or video world models without losing statistical guarantees. Built on novel combinations of sequential and graphical multiple-hypothesis testing, it needs far fewer real-robot trials on benchmarks.
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