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arXiv · 2608.16503

NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation

Cong Zhao·Shuai Tian·Xu Zhang·Baocheng Ni·Xinguo Song·Xueying Sun·Shu Jiang·Shouchang Yang·Bo Tang·Jin Deng·Ge Zhu·YongCheng Wang·Jin Xu·Ri Yang
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Abstract

Real-world deployment of Vision-Language-Action (VLA) models is often bottlenecked by efficiency-performance trade-offs, cross-embodiment generalization, and execution smoothness. We present NebulaVLA, an asynchronous dual-frequency architecture that decouples high-level semantic reasoning from low-level action control, optimizing computational resources and modularity. To bridge semantic gaps across heterogeneous robots, we introduce GESTURE-7, a unified language-grounded action representation. Furthermore, our Guide Action algorithm enforces kinematic continuity via mask-based smoothness constraints. Comprehensive evaluations demonstrate that NebulaVLA significantly outperforms synchronous baselines, achieving an 85.5\% average success rate on LIBERO-Plus and accelerating action generation by \textasciitilde 2.7$\times$. This asynchronous design enables highly efficient and responsive control for practical robotics.

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Cong Zhao, Shuai Tian, Xu Zhang, Baocheng Ni, Xinguo Song, Xueying Sun, Shu Jiang, Shouchang Yang, Bo Tang, Jin Deng, Ge Zhu, YongCheng Wang, Jin Xu, Ri Yang. 2026-08-17. NebulaVLA: A Dual-Frequency Vision-Language-Action Model With Guide Action for Robotic Manipulation. https://arxiv.org/abs/2608.16503

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