RouterVLA: Budgeted Commissioning and Expert Onboarding for Growing VLA Pools

Abstract:Robotic teams often maintain several vision-language-action policies but still deploy one global winner. We study two recurring decisions: which expert to deploy for a new condition and which candidate to add to the pool. RouterVLA combines a split-clean prior and outcome-disjoint probes with onboarding that credits only failures the incumbent pool cannot handle. Under an exactly cost-matched probe budget, it reaches 60.53\% held-out success, a $+1.64\pp$ gain over a semantic shortlist. Both criteria independently converge on the same five experts, confirming that the candidates best positioned to cover the base pool's blind spots are also broadly capable.

Robotics (cs.RO)

arXiv:2606.27355 [cs.RO]

(or arXiv:2606.27355v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.27355 Focus to learn more arXiv-issued DOI via DataCite

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