arxivcs.RO2026-07-03
DREAMSTEER: Latent World Models Can Steer VLA Policies During Deployment Without Any Finetuning
Hanchen Cui, Sergio Arnaud, Arjun Majumdar, Daniel Dugas, Elie Aljalbout, Karthik Desingh, et al.
Pretrained vision-language-action (VLA) policies show promising zero-shot generalization, but often fail under deployment-time distribution shift, leading to decreased robustness and inconsistent instruction following. While prior work commonly tackles this by finetuning on in-di…