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

Key points

• We propose SearchWorld, a recurrent state-space world model that connects explicit spatial memory with value-guided imagination.1
• The model maintains BEV exploration and obstacle memory and decodes a task-aware spatial value layer to guide search.1
• Training progresses from world-model learning to expert imitation and imagination-based exploration refinement.1
• By grounding imagination in explicit spatial representations, SearchWorld enables UAV agents to plan prospectively rather than react.1

Sources (1)

• [1]SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:51 AM

  Autonomous unmanned aerial vehicle (UAV) object search involves a closed loop of perception, decision-making, and action under partial observability.

  We propose SearchWorld, a recurrent state-space world model that connects explicit spatial memory with value-guided imagination.

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