Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
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Publication:6331557
arXiv1912.10116MaRDI QIDQ6331557
Nikolay Atanasov, Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti
Publication date: 20 December 2019
Abstract: This paper focuses on learning a model of system dynamics online while satisfying safety constraints.Our motivation is to avoid offline system identification or hand-specified dynamics models and allowa system to safely and autonomously estimate and adapt its own model during online operation.Given streaming observations of the system state, we use Bayesian learning to obtain a distributionover the system dynamics. In turn, the distribution is used to optimize the system behavior andensure safety with high probability, by specifying a chance constraint over a control barrier function.
Has companion code repository: https://github.com/wecacuee/Bayesian_CBF
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