Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
This addresses the need for autonomous systems to safely adapt their models during operation without offline identification, which is incremental as it builds on existing safety and learning methods.
The paper tackles the problem of learning system dynamics online while ensuring safety, using Bayesian learning to obtain a distribution over dynamics and optimizing behavior with chance constraints on control barrier functions to achieve safety with high probability.
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.