← 研踪 PaperTrailarXiv:2603.09542 · v4
NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models
首次提交 2026-03-10 19:51
版本更新 2026-11-11 16:49
雷达观测 2026-11-13 14:16
摘要
Vision-Language-Action (VLA) models ground natural-language instructions in visual observations to generate actions for robotic manipulation. On long-horizon tasks, existing VLA models 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 that parses the instruction into a primitive plan tracked by a gated monotone stage pointer, a Neuro-Symbolic Solver that conditions a chunked VLA policy on the active sub-instruction, and Hierarchical Joint Policy Optimization, which fits the pointer on demonstration segments and refines the action policy with success-filtered online learning. Even when trained on few demonstrations, NS-VLA clearly leads on LIBERO and the held-out LIBERO-Plus (76.5% and 51.8% against 71.2% and 45.4% for its backbone), exceeds the strongest baseline by 12.7 points on long-horizon tasks, and raises real-robot success from 40.0% to 60.0%. Our code is publicly available at https://github.com/Zuzuzzy/NS-VLA.
作者备注：Accepted by NeurIPS 2026 main conference
期刊信息：NeurIPS 2026
观测到的版本
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v4 · 2026-11-11 16:49 · 最新观测版本
NS-VLA: Towards Neuro-Symbolic Vision-Language-Action Models
Vision-Language-Action (VLA) models ground natural-language instructions in visual observations to generate actions for robotic manipulation. On long-horizon tasks, existing VLA models 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 that parses the instruction into a primitive plan tracked by a gated monotone stage pointer, a Neuro-Symbolic Solver that conditions a chunked VLA policy on the active sub-instruction, and Hierarchical Joint Policy Optimization, which fits the pointer on demonstration segments and refines the action policy with success-filtered online learning. Even when trained on few demonstrations, NS-VLA clearly leads on LIBERO and the held-out LIBERO-Plus (76.5% and 51.8% against 71.2% and 45.4% for its backbone), exceeds the strongest baseline by 12.7 points on long-horizon tasks, and raises real-robot success from 40.0% to 60.0%. Our code is publicly available at https://github.com/Zuzuzzy/NS-VLA.
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