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Found 5 papers, 0 papers with code
State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE
We develop a computationally efficient learning-based forward-backward stochastic differential equations (FBSDE) controller for both continuous and hybrid dynamical (HD) systems subject to stochastic noise and state constraints.
Data-Efficient Control Barrier Function Refinement
Then, a probability distribution based on the priority score of the data points is used to sample data and update the learned CBF.
Learning a Better Control Barrier Function
With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient.
State Constrained Stochastic Optimal Control Using LSTMs
In this paper, we propose a new methodology for state constrained stochastic optimal control (SOC) problems.
GRP Model for Sensorimotor Learning
Learning from complex demonstrations is challenging, especially when the demonstration consists of different strategies.
