Axiom-0: 視覚-言語-行動ポリシーのための階層的自己洗練
Axiom-0: Hierarchical Self-Refinement for Vision-Language-Action Policies
VLAモデルにおいて、VLM内部表現と行動生成インターフェースを階層的に自己洗練させることで、未知のタスク構成での成功率を向上させた研究。
著者: Renbin Shu, Jing Yang, Xiangyuan You, Guolin Qiu, Yufei Xia, Yiming Zhu, Nanjun Dai, Fei Zhang, Feng Wu, Keke Tang
分類: cs.RO, cs.CV
原文アブストラクト
Reliable manipulation requires policies that generalize across tasks, layouts, and object interactions. Vision-language-action (VLA) models combine pretrained multimodal understanding with learned control, but their performance remains limited on unfamiliar task compositions. We present Axiom-0, a VLA policy that addresses both the quality of its representations and their use in action generation through hierarchical self-refinement. Intra-VLM Representation Refinement (IVRR) strengthens selected intermediate features through consistency supervision with learned feature projections during training. VLM-Action Interface Refinement (VAIR) recursively updates the resulting conditioning before action generation. Joint optimization of the supervised VLM layers, VAIR, and the action generator connects the two components. On RoboCasa365, Axiom-0 achieves 62.32% overall success in our evaluation, exceeding the highest published leaderboard score (Paimon-0, 58.1%) by 4.22 percentage points and the Xiaomi-Robotics-1 reference by 4.92 points. Under the evaluated profiling setup, mean action-chunk generation latency increases by 1.7% over Xiaomi-Robotics-1. These results demonstrate improved task completion with modest inference overhead.