Authors:Ziyue Zhu, Shangyang Wu, Shuai Zhao, Zhiqiu Zhao, Jian Zhang, Shengjie Li, Yi Wang, Anh Tuan Luu, Xinliang Zhou, Fang Li, Haoran Luo
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.
From: Ziyue Zhu [view email]
[v1]
Tue, 10 Mar 2026 11:51:54 UTC (1,933 KB)
[v2]
Wed, 2 Sep 2026 17:11:37 UTC (9,941 KB)
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