RA-VLA: テスト時適応のための検索拡張VLA
RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation
新しいタスク分布に弱いVLAモデルの問題を解決するため、行動に合わせた文脈検索と実行パイプラインを統合した検索拡張VLAフレームワークを提案し、LIBEROベンチマークと実環境で高い成功率と効率を実証した。
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著者: Sanghwan Jang, Minjin Jeon, Minsoo Kim, Seongjin Choi, Dongha Kim, Hwanjo Yu
分類: cs.RO
原文アブストラクト
Vision-Language-Action (VLA) models provide a versatile foundation for general robotic manipulation, yet they exhibit significant brittleness when confronted with novel task distributions. While In-Context Imitation Learning (ICIL) offers a training-free alternative, existing frameworks suffer from an adaptation bottleneck that hinders the effective translation of expert context to executable actions. This failure originates from superficial retrieval mechanisms and an inherent behavioral inertia that anchors the policy to its pre-trained priors. To address these limitations, we present RA-VLA, a retrieval-augmented VLA framework that integrates behavior-aligned context retrieval with a grounded execution pipeline. By enforcing faithful adherence to functional cues within a scalable architecture, RA-VLA facilitates seamless task adaptation while preserving inference efficiency. Our empirical evaluations across the LIBERO benchmark and a real-world UR5e environment demonstrate that RA-VLA achieves superior success rates and computational efficiency, establishing a robust framework for training-free robotic adaptation.