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arXiv · 2603.09542

NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models

Ziyue Zhu·Shangyang Wu·Shuai Zhao·Zhiqiu Zhao·Jian Zhang·Shengjie Li·Yi Wang·Anh Tuan Luu·Xinliang Zhou·Fang Li·Haoran Luo
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Abstract

Vision-Language-Action (VLA) models are formulated to ground instructions in visual context and generate action sequences for robotic manipulation. Despite recent progress, VLA models still face structure-blind backbones, backbone-bound generalization, and flat single-objective optimization. To address these challenges, we propose a novel Neuro-Symbolic Vision-Language-Action (NS-VLA) framework. It introduces a Neuro-Symbolic Encoder for plan-constrained primitive inference, a Neuro-Symbolic Solver that conditions a backbone-agnostic policy on the active primitive, and Hierarchical Joint Policy Optimization with reward-granularity matching. Experiments on robotic manipulation benchmarks demonstrate that NS-VLA outperforms previous methods in both one-shot training and data-perturbed settings, while simultaneously exhibiting superior zero-shot generalizability and expanded exploration space. Our code is publicly available.

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Ziyue Zhu, Shangyang Wu, Shuai Zhao, Zhiqiu Zhao, Jian Zhang, Shengjie Li, Yi Wang, Anh Tuan Luu, Xinliang Zhou, Fang Li, Haoran Luo. 2026-03-10. NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models. https://arxiv.org/abs/2603.09542

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