Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics
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 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.
Cite
Text
Khojasteh et al. "Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.Markdown
[Khojasteh et al. "Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics." Proceedings of the 2nd Conference on Learning for Dynamics and Control, 2020.](https://mlanthology.org/l4dc/2020/khojasteh2020l4dc-probabilistic/)BibTeX
@inproceedings{khojasteh2020l4dc-probabilistic,
title = {{Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics}},
author = {Khojasteh, Mohammad Javad and Dhiman, Vikas and Franceschetti, Massimo and Atanasov, Nikolay},
booktitle = {Proceedings of the 2nd Conference on Learning for Dynamics and Control},
year = {2020},
pages = {781-792},
volume = {120},
url = {https://mlanthology.org/l4dc/2020/khojasteh2020l4dc-probabilistic/}
}