arXiv Robotics — research abstracts· Renbin Shu, Jing Yang, Xiangyuan You, Guolin Qiu, Yufei Xia, Yiming Zhu, Nanjun Dai, Fei Zhang, Feng Wu, Keke Tang·· 7 hours agoEditorial score62
Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies
Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies
Summary
Axiom-0 is a vision-language-action (VLA) policy that enhances task generalization through hierarchical self-refinement. It improves representation quality and action generation via intra-VLM representation refinement and VLM-action interface refinement. On RoboCasa365, it achieves 62.32% overall success, surpassing Paimon-0 by 4.22 percentage points and Xiaomi-Robotics-1 by 4.92 points, with only a 1.7% increase in action-chunk generation latency.
Source: arXiv Robotics — research abstracts · Read original article ↗
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Source：arXiv Robotics — research abstracts · arxiv.org