English(EN) X-Fly: Learning to Reach and Stop in Full-Chunk Aerial Vision-Language Navigation

X-Fly框架增强了空中视觉语言导航代理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了X-Fly，一个旨在改进空中视觉语言导航（VLN）代理的新型训练框架。该框架通过在重新规划之前执行完整的动作块来解决模型推理的高成本问题，从而显著减少查询时间。X-Fly采用分阶段训练方法，包括用于标记学习者访问状态的Recovery DAgger和用于精炼策略的Flow-GRPO，与现有方法相比，在已见和未见空中导航任务的成功率方面取得了显著提高。 AI

影响这项研究通过降低计算成本和提高导航精度，有望带来更高效、更自主的航空系统。

排序理由该集群包含一篇详细介绍空中视觉语言导航新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍空中视觉语言导航新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Ruibin Ju, Ruifeng Cai, Min Xu, Lin Wang · 2026-10-16 04:00

X-Fly：学习在全块空中视觉语言导航中到达和停止

arXiv:2610.21450v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) agents built on large vision-language models are expensive to query. Executing a complete predicted action chunk before replanning amortizes this cost, reducing model inference from 195 to 4…

本文如何被排名

报道来源 [1]

X-Fly：学习在全块空中视觉语言导航中到达和停止

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