LASER: Learning Active Sensing for Continuum Field Reconstruction

Researchers propose LASER, a reinforcement learning framework that treats adaptive sensor placement as a decision problem, using a learned world model to simulate measurement scenarios and guide where sensors should move next. The approach combines POMDPs with latent-space planning to reconstruct physical fields more efficiently than fixed sensor arrays.

MentionsLASER · POMDP · reinforcement learning

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