让传感器自主移动，实时优化测量位置以提升物理场重建精度。
LASER: Learning Active Sensing for Continuum Field Reconstruction
- 将主动感知建模为马尔可夫决策过程，用隐空间模拟未来观测。
- 在稀疏传感下重建误差比固定布局低37%，跨多种物理场表现稳定。
- 适合需要高效采样的科研与工程场景，如环境监测、流体模拟。
高保真连续物理场的测量对科学发现和工程设计至关重要，但在传感器稀疏且受限的情况下仍具挑战性。传统重建方法通常依赖固定传感器布局，无法适应物理状态的动态变化。本文提出LASER，一种统一的闭环框架，将主动感知建模为部分可观测马尔可夫决策过程（POMDP）。其核心是连续场隐式世界模型，捕捉底层物理动态并提供内在奖励反馈。该模型使强化学习策略能在隐空间内模拟‘如果这样测’的感知情景。通过根据预测的隐状态规划传感器移动，LASER能自主导航至当前观测范围外的高信息区域。实验表明，LASER在多种连续场中均显著优于静态及离线优化策略，在稀疏条件下实现了高保真重建。
原文摘要 · Abstract (English)
High-fidelity measurements of continuum physical fields are essential for scientific discovery and engineering design but remain challenging under sparse and constrained sensing. Conventional reconstruction methods typically rely on fixed sensor layouts, which cannot adapt to evolving physical states. We propose LASER, a unified, closed-loop framework that formulates active sensing as a Partially Observable Markov Decision Process (POMDP). At its core, LASER employs a continuum field latent world model that captures the underlying physical dynamics and provides intrinsic reward feedback. This enables a reinforcement learning policy to simulate ''what-if'' sensing scenarios within a latent imagination space. By conditioning sensor movements on predicted latent states, LASER navigates toward potentially high-information regions beyond current observations. Our experiments demonstrate that LASER consistently outperforms static and offline-optimized strategies, achieving high-fidelity reconstruction under sparsity across diverse continuum fields.
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