LASER: Learning Active Sensing for Continuum Field Reconstruction
Summary: arXiv:2604.19355v1 Announce Type: cross
Abstract: 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.
Introduction
In the realm of scientific research and engineering, accurate measurements of physical fields are crucial. However, capturing high-fidelity data remains a significant challenge, especially when dealing with limited sensing capabilities. Traditional methods often rely on static sensor configurations, which fall short in adapting to the dynamic nature of physical phenomena.
The LASER Framework
LASER, which stands for Learning Active Sensing for Continuum Field Reconstruction, introduces a novel approach to this problem. By framing active sensing as a Partially Observable Markov Decision Process (POMDP), LASER allows for a more flexible and responsive sensing strategy. This framework enables the system to make informed decisions about sensor placements based on predicted outcomes.
Core Components of LASER
The LASER framework is built upon several key components:
- Continuum Field Latent World Model: This model captures the underlying dynamics of physical fields and is essential for predicting future states.
- Intrinsic Reward Feedback: By providing feedback based on the captured dynamics, LASER enhances the learning process, guiding the system toward optimal sensing strategies.
- Reinforcement Learning Policy: This policy simulates potential sensing scenarios, allowing LASER to explore “what-if” situations that inform sensor placement decisions.
Advantages of LASER
One of the standout features of LASER is its ability to adapt to evolving physical states. Unlike static methods that are limited by their initial configurations, LASER dynamically adjusts its sensor movements based on real-time predictions. This proactive approach allows LASER to:
- Identify high-information regions that may be overlooked by traditional methods.
- Achieve high-fidelity reconstructions even under conditions of data sparsity.
- Enhance the efficiency of measurements across various continuum fields.
Experimental Results
The effectiveness of LASER has been validated through extensive experiments. Results indicate that LASER consistently surpasses static and offline-optimized strategies, demonstrating its superiority in reconstructing continuum fields with high fidelity. The experiments highlight the framework’s robust performance across diverse scenarios, establishing it as a promising solution for active sensing challenges.
Conclusion
In conclusion, LASER represents a significant advancement in the field of active sensing for continuum field reconstruction. By integrating reinforcement learning with a flexible sensing strategy, LASER not only addresses the limitations of traditional methods but also paves the way for future developments in high-fidelity measurement techniques. The ongoing research and potential applications of LASER could transform how we approach scientific discovery and engineering design.