LASER: Learning Active Sensing for Continuum Field Reconstruction
Provides a unified framework for adaptive sensor placement in scientific and engineering applications where high-fidelity field reconstruction is needed with limited sensors.
LASER formulates active sensing as a POMDP, using a latent world model and reinforcement learning to adapt sensor movements for high-fidelity reconstruction of continuum fields under sparse sensing, consistently outperforming static and offline-optimized strategies.
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.