arXiv:2604.19355v2 Announce Type: replace 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 dy
The continuous advancements in AI and machine learning, particularly in reinforcement learning and latent world models, enable the development of sophisticated active sensing frameworks like LASER.
This development allows for more efficient and adaptive data collection in complex physical environments, crucial for scientific discovery, engineering, and autonomous systems.
Traditional fixed-layout sensing is being augmented or replaced by adaptive, AI-driven active sensing, leading to higher fidelity measurements with fewer resources.
- · AI/ML researchers
- · Robotics and automation
- · Scientific research institutions
- · Aerospace and defence
- · Providers of fixed-sensor infrastructure
- · Traditional data acquisition methods
Improved efficiency and accuracy in data collection for complex systems like climate modeling, material science, and autonomous navigation.
Reduced operational costs and enhanced performance for AI-driven robots and smart infrastructure projects.
Acceleration of research and development cycles in scientific domains that rely heavily on physical field measurements, leading to new discoveries and industrial applications.
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Read at arXiv cs.LG