S$^2$-VLA: State-Space Guided Vision-Language-Action Models for Long-Horizon Manipulation
Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in long-horizon tasks due to cumulative error propagation. This limitation largely arises from static feature fusion mechanisms that rely on fixed weights to combine visual, language, and action representations, preventing the model from adapting to different phases of task execution. To address this limitation, we propose S$^2$-VLA, a framework that introduces a State-Space Guided Adaptive Attention (SSGAA) mechanism. SSGAA maintains a belief state that tracks task progression and generates dynamic gating weights to adaptively fuse information fr...
| Authors | Zhipeng Xie, Zongyi Han, Xiangyi Wei et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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