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AnyRank: A Reliable Ranking Method for Robot Policies Based on Sequential Testing

AnyRank proposes a sequential method for multi-policy ranking that adaptively allocates trials to policies that are hard to distinguish, provides a (partial) ranking at any stopping time, and bounds the probability of an incorrect ranking. The method can optionally use prior information (such as expert intuition, a small set of held-out hardware evaluations, or policy rollouts from a simulator or an action-conditioned video world model) to improve hardware evaluation efficiency, and inaccurate priors do not break its statistical guarantees.

Part of the event: AnyRank Reliably Ranks Robot Policies Using Sequential Testing

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