Asst Prof @ UMaine Robotics, Autonomous Vehicles, Machine Learning

Probabilistic Safety Constraints

How do you ensure safety when the robot behavior is only known with uncertainty? We propose Probabilistic Safety Constraints for High Relative Degree System Dynamics. We show how to learn a system with a Bayesian Learning method that keeps track of uncertainty while ensuring safety upto an acceptable risk factor, for example, 99.999%. We use the framework of Control Barrier Functions and extend it to higher-order relative degree systems while propagating uncertainty in model dynamics to the safety constraints.

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 allow a 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 distribution over the system dynamics. In turn, the distribution is used to optimize the system behavior and ensure safety with high probability, by specifying a chance constraint over a control barrier function. Paper Bibtex

Mohammad Javad Khojasteh*, Vikas Dhiman*, Massimo Franceschetti, Nikolay Atanasov "Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics"

Presentation in ITA on Feb 4