GuidedVLA: Specifying Task-Relevant Factors via Plug-and-Play Action Attention Specialization

2026-05-12

AI Summary

Existing vision-language-action (VLA) models tend to overfit to spurious visual cues in the absence of explicit guidance, limiting their generalization capabilities. This work proposes GuidedVLA, a novel framework that explicitly decouples the action decoder into multiple plug-and-play, specialized attention heads and introduces handcrafted auxiliary supervision signals to guide each head toward task-critical factors—such as object localization, spatial geometry, and temporal skill logic. By promoting attention head specialization within an end-to-end robotic learning pipeline, GuidedVLA substantially outperforms strong baselines in both simulation and real-world environments, achieving higher success rates on both in-distribution and out-of-distribution tasks. Moreover, the quality of each specialized factor is positively correlated with overall task performance.

Abstract

Vision-Language-Action (VLA) models aim for general robot learning by aligning action as a modality within powerful Vision-Language Models (VLMs). Existing VLAs rely on end-to-end supervision to implicitly enable the action decoding process to learn task-relevant features. However, without explicit guidance, these models often overfit to spurious correlations, such as visual shortcuts or environmental noise, limiting their generalization. In this paper, we introduce GuidedVLA, a framework designed to manually guide the action generation to focus on task-relevant factors. Our core insight is to treat the action decoder not as a monolithic learner, but as an assembly of functional components. Individual attention heads are supervised by manually defined auxiliary signals to capture distinct factors. As an initial study, we instantiate this paradigm with three specialized heads: object grounding, spatial geometry, and temporal skill logic. Across simulation and real-robot experiments, GuidedVLA improves success rates in both in-domain and out-of-domain settings compared to strong VLA baselines. Finally, we show that the quality of these specialized factors correlates positively with task performance and that our mechanism yields decoupled, high-quality features. Our results suggest that explicitly guiding action-decoder learning is a promising direction for building more robust and general VLA models.

Problem

• Vision-Language-Action
• action decoding
• spurious correlations
• generalization
• task-relevant factors

Innovation

• Vision-Language-Action
• action attention specialization
• task-relevant guidance
• modular decoder
• auxiliary supervision