patch-policy-6fb6d5d7·1 events·first seen Aliases: Patch Policy
Patch Policy is a new robot learning architecture that enables transformer-based policies to consume dense pre-trained Vision Transformer patch tokens directly, without the overhead of a full vision-language model backbone. A block-causal attention mask preserves temporal causality while allowing the model to attend over many patch tokens per observation. Across four simulated and three real-world environment suites, the method achieves a 40% relative improvement over global-pooled representation baselines and outperforms fine-tuned OpenVLA-OFT by 18% while using roughly 0.7% of its parameters. The work addresses a practical gap between lightweight robot policies and expensive VLA models.