arXiv:2512.08013v2 Announce Type: replace-cross Abstract: 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. The resulting posterior samples are used to formulate a scenario-based optimal control problem that accounts for the
The continuous drive towards more autonomous and robust AI systems necessitates advancements in handling uncertainty with limited data, a critical bottleneck across many applications.
This research addresses a fundamental challenge in deploying AI for real-world control systems where data is scarce and unreliable, impacting both safety and efficiency.
The ability to learn complex system dynamics from infrequent, noisy output measurements, and subsequently perform uncertainty-aware optimal control, significantly broadens the applicability of AI in critical infrastructure and robotics.
- · AI developers
- · Robotics industry
- · Autonomous systems sector
- · Industrial control systems
- · Systems requiring extensive sensor arrays
- · Brittle control systems
- · Industries with high data acquisition costs
More robust and reliable AI-driven control systems will emerge in complex environments.
Reduced operational costs and increased safety in sectors like manufacturing, aerospace, and defense due to improved autonomous decision-making.
Accelerated development of general-purpose AI agents capable of adaptable control in highly uncertain, real-world conditions, collapsing more white-collar workflows.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG