具身开发者社区

外观

论文arXiv 2610.0933521 天前

SearchWorld：通过 World Models 实现空间价值扎根想象的 UAV 目标搜索 ​

SearchWorld用连接显式空间记忆与价值引导想象的世界模型，提升UAV-ON上的无人机目标搜索。

AI 整理
  • 具身模型
  • 端侧部署

原文链接：SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models（arXiv 2610.09335 · 发布日期：2026-10-07）

SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models

SearchWorld用连接显式空间记忆与价值引导想象的世界模型，提升UAV-ON上的无人机目标搜索。

核心要点 ​

  • 城市UAV目标搜索受部分可观测、大范围、窄视角、复杂3D几何和开放指令影响。
  • 方法维护BEV探索与障碍记忆，解码任务感知空间价值层来引导搜索。
  • 认知-动作网络利用学习到的空间价值先验，经想象展开改进策略且不单独训练标量评论器。
  • 在UAV-ON上成功率达23.8%，优于最强已发表智能体的19.5%，未见场景为19.9%。

验证范围与局限：验证范围为UAV-ON上的UAV目标搜索，包括未见场景评估。

英文摘要 (Abstract) ​

Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability. Urban environments pose several challenges: large search areas and narrow egocentric views limit coverage, dense 3D geometry constrains safe motion, and open-world instructions require identifying a specific target among distractors. Many existing methods mitigate partial observability through explicit maps or memory representations, yet remain largely reactive, reasoning over past observations without explicitly predicting future states. World models enable prospective reasoning through imagined rollouts. However, image-generating world models can incur high inference latency, while spatially grounded planning remains challenging for latent world models. We propose SearchWorld, a recurrent state-space world model that connects explicit spatial memory with value-guided imagination. The model maintains BEV exploration and obstacle memory and decodes a task-aware spatial value layer to guide search. A cognition-action network uses this learned spatial value prior to improve the policy through imagined rollouts, without training a separate scalar critic. Training progresses from world-model learning to expert imitation and imagination-based exploration refinement. On UAV-ON, SearchWorld improves the success rate to 23.8% (19.5% for the strongest published agent) and raises oracle success to 35.5%, while remaining robust on unseen scenes (19.9% success rate). By grounding imagination in explicit spatial representations, SearchWorld enables UAV agents to plan prospectively rather than react.

作者：Yatai Ji, Zhengqiu Zhu, Yong Zhao, Yue Hu, Fanglong Yao, Chen Gao, Pengfei Zhu, Quanjun Yin

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2610.09335：SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models访问 2026-10-21