Closed-Loop Adaptive Sampling on a Generative Ocean Model
Overview
This research introduces a multi-robot closed-loop adaptive sampling framework leveraging learned generative ocean models. The system utilizes a single model for forecasting, uncertainty estimation, planning, and measurement assimilation within a latent space that is decoded to physical ocean states. By constructing A- or D-optimal sampling objectives based on ensemble statistics and selecting locations subject to robot reachability under predicted currents, the approach assimilates collected measurements back into the latent state using observation-conditioned diffusion sampling. Experiments across 120 test scenarios demonstrate that this method generally outperforms static, random, and systematic-coverage baselines with minimal planning overhead, though results also highlight scenarios where model uncertainty diverges from forecast error.
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Reported quantity · test scenarios: 120 other · reported trials
“g observation-conditioned diffusion sampling. Across 120 test scenarios A- and D-optimal sampling generally outperforms static, random, and systematic-coverage baselines while adding little planning overhead. Our experiments uncover several scenarios in which model uncertainty diverges from forecast error, motivating future work in uncertainty quantification.”
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- arXiv Robotics — research abstractsSignalClosed-Loop Adaptive Sampling on a Generative Ocean Model
This research introduces a multi-robot closed-loop adaptive sampling framework leveraging learned generative ocean models. The system utilizes a single model for forecasting, uncertainty estimation, planning, and measurement assimilation within a latent space that is decoded to physical ocean states. By constructing A- or D-optimal sampling objectives based on ensemble statistics and selecting locations subject to robot reachability under predicted currents, the approach assimilates collected measurements back into the latent state using observation-conditioned diffusion sampling. Experiments across 120 test scenarios demonstrate that this method generally outperforms static, random, and systematic-coverage baselines with minimal planning overhead, though results also highlight scenarios where model uncertainty diverges from forecast error.
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