RA-VLA: Retrieval-Augmented VLA for Test-Time Adaptation

Abstract

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 actions. This failure originates from superficial retrieval mechanisms and an entrenched *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 unifies behavior-aligned context retrieval with a grounded execution pipeline. By enforcing strict adherence to functional cues within a scalable architecture, our framework facilitates seamless task adaptation while preserving inference efficiency. Evaluations across the LIBERO benchmark and a real-world UR5e environment demonstrate that RA-VLA achieves superior success rates and computational efficiency, providing a robust framework for training-free robotic adaptation.