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Found 5 papers, 1 papers with code
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process
We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process.
Physics-Informed Kernel Embeddings: Integrating Prior System Knowledge with Data-Driven Control
Our proposed approach incorporates prior knowledge of the system dynamics as a bias term in the kernel learning problem.
Characterizing Within-Driver Variability in Driving Dynamics During Obstacle Avoidance Maneuvers
We propose an easily computable modeling framework which takes advantage of a metric to assess variability in individual human response in a dynamic task that subjects repeat over several trials.
SOCKS: A Stochastic Optimal Control and Reachability Toolbox Using Kernel Methods
We present SOCKS, a data-driven stochastic optimal control toolbox based in kernel methods.
Data-Driven Chance Constrained Control using Kernel Distribution Embeddings
We present a data-driven algorithm for efficiently computing stochastic control policies for general joint chance constrained optimal control problems.
