AI RESEARCH
Learning Dynamics from Infrequent Output Measurements for Uncertainty-Aware Optimal Control
arXiv CS.LG
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ArXi:2512.08013v2 Announce Type: replace-cross Reliable optimal control is challenging when the dynamics of a nonlinear system are unknown and only infrequent, noisy output measurements are available. This work addresses this setting of limited sensing by formulating a Bayesian prior over the continuous-time dynamics and latent state trajectory in state-space form and updating it through a targeted Metropolis-Hastings sampler equipped with a numerical ODE integrator.