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Stochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations
Control barrier functions are widely used to synthesize safety-critical controls.
Data-Driven Learning of Safety-Critical Control with Stochastic Control Barrier Functions
More specifically, we propose a data-driven stochastic control barrier function (DDSCBF) framework and use supervised learning to learn the unknown stochastic dynamics via the DDSCBF scheme.
Safety-Critical Control of Stochastic Systems using Stochastic Control Barrier Functions
We propose a notion of stochastic control barrier functions (SCBFs)and show that SCBFs can significantly reduce the control efforts, especially in the presence of noise, compared to stochastic reciprocal control barrier functions (SRCBFs), and offer a less conservative estimation of safety probability, compared to stochastic zeroing control barrier functions (SZCBFs).
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks
A na\"ive way of solving a motion planning problem with LTL specifications using reinforcement learning is to sample a trajectory and then assign a high reward for training if the trajectory satisfies the entire LTL formula.
