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• W2996109218 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." @default.
• W2996109218 created "2019-12-26" @default.
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• W2996109218 date "2019-12-20" @default.
• W2996109218 modified "2023-09-23" @default.
• W2996109218 title "Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics" @default.
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• W2996109218 hasPublicationYear "2019" @default.
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