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arXiv Computer Vision By An Lanji, Dawei Liu, Jin Li, Haoran Xu, Mei Chen, Yu Tian

AWM-VLA: AlignedWorld Modeling for Efficient and Explainable Vision-Language-Action Policies

Read the original on arXiv Computer Vision →

AWM‑VLA introduces a unified framework that embeds aligned world modeling directly into a diffusion‑transformer vision‑language‑action policy. By adding learnable future tokens aligned with vision‑language embeddings of future observations, the policy can anticipate long‑term consequences while generating actions. The method extends this with an object‑centric alignment objective and a principled weighting scheme, achieving up to 21% higher success rates on RoboCasa and humanoid tabletop benchmarks and producing object‑centric rationales preferred by human raters in 83% of cases.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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