Newest papers

FocusVLA: Future-Intent Guided Dynamic View Routing for Vision-Language-Action Models

Huixin Xu, Ziya Yu, Peng Li · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31423 · cited by 0 works (OpenAlex) · open-access PDF

German

Da zuverlässige Manipulation eine ausreichende Beobachtung von Objekten, Endeffektoren und ihren räumlichen Beziehungen erfordert, werden Multi-View-Eingänge weit verbreitet, um ergänzende räumliche Informationen zu liefern. Unsere Experimente zeigen jedoch, dass bestehende VLA-Modelle solche multi-View-Kernkomplementarität nicht voll ausnutzen. Die Politik neigt dazu, sich ständig auf die Handgelenkansicht zu verlassen, ohne die Fähigkeit zu haben, die globale-lokale Ansichtsabhängigkeit dynamisch über Manipulationsstufen hinweg anzupassen, was die Komplementarität und Robustheit der Bühne einschränkt. Um dieses Problem anzugehen, schlagen wir FocusVLA, ein Stadium-bewusstes Multi-View-Routing-

Machine translation (Meta M2M100); the English original follows.

Abstract

Vision-language-action (VLA) models have become an important paradigm for robotic manipulation by unifying visual perception, language understanding, and action generation. Since reliable manipulation requires sufficient observation of objects, end-effectors, and their spatial relationships, multi-view inputs are widely used to provide complementary spatial information. However, our experiments show that existing VLA models do not fully exploit such multi-view complementarity. The policy tends to persistently rely on the wrist view, lacking the ability to dynamically adjust global-local view dependency across manipulation stages, which limits stage adaptability and robustness. To address this issue, we propose FocusVLA, a stage-aware multi-view routing framework. FocusVLA predicts future interaction intent from historical actions and uses it to guide dynamic view routing during action generation. By adaptively coordinating global and local observations across different task stages, FocusVLA improves multi-view utilization. On RoboTwin 2.0, FocusVLA improves the average success rate over the strongest baseline $π_{0.5}$ by 10.10 pp. In real-world evaluation, FocusVLA improves the success rate from 71.7\% to 85.0\%, achieving a 13.3\% gain over $π_{0.5}$. Moreover, the intent prediction and dynamic routing modules of FocusVLA can be integrated into existing VLA frameworks without modifying their core architectures.

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Topics: Computer science, Exploit, Adaptability, Task (project management), Routing (electronic design automation), Dependency (UML)

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