AnyRank: Sequential Testing Ranks Robot Policies Reliably With Up to 50% Fewer Trials
ZeYanjie · x · 2026-10-15
arXiv paper AnyRank (Badithela et al.) addresses a core pain point in robot policy evaluation: hardware trials are expensive, and rankings from limited samples often yield wrong conclusions. Key ideas:
- Sequential multi-policy ranking that adaptively allocates trials to hard-to-separate policies;
- Provides (partial) rankings at any stopping time while bounding the probability of incorrect ranking;
- Optionally exploits priors—expert intuition, small held-out hardware evaluations, or rollouts from simulators or action-conditioned video world models—without invalidating statistical guarantees even if the prior is wrong;
- Combines sequential testing with multiple-hypothesis graphical testing in theory.
Experiments show up to 50% fewer hardware evaluations versus existing methods. Context: 304 robotics papers hit arXiv that day alone.
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