Closed-Loop Adaptive Sampling on a Generative Ocean Model
Closed-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.
The paper proposes a closed-loop adaptive sampling framework for multi-robot systems using learned generative ocean models to forecast states and estimate uncertainty in latent space, enabling A- and D-optimal planning that outperforms static baselines across 120 test scenarios while accounting for robot reachability under predicted currents.
Source: arXiv Robotics — research abstracts · Read original article ↗
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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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nity to use rich predictive forecasts directly in closed-loop robotic sampling. However, these models do not natively provide the explicit uncertainty representations assumed by classical informative-planning methods, and their forecasts often evolve in learned latent spaces rather than directly in physical ocean variables. We develop a multi-robot closed-loop adaptive sampling framework that uses a single learned ge
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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.
Open source S5
nerative model for forecasting, uncertainty estimation, planning, and measurement assimilation. The model forecasts an ensemble in latent space, decodes it to physical ocean states, and uses ensemble statistics to construct A- or D-optimal sampling objectives. We select sampling locations subject to robot reachability under predicted currents. The collected measurements are assimilated back into the latent state usin
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Closed-Loop Adaptive Sampling on a Generative Ocean Model
Open source S1
arXiv:2611.05776v1 Announce Type: new Abstract: Adaptive sampling can substantially improve the value of sparse ocean observations by directing mobile robots toward measurements that are most informative for an evolving environment. Recent learned generative ocean models can produce high-resolution forecasts and ensembles at substantially lower computational cost than traditional numerical models, creating an opportu
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Source：arXiv Robotics — research abstracts · arxiv.org