Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics

Mohammad Javad Khojasteh, Vikas Dhiman, Massimo Franceschetti, Nikolay Atanasov
; Proceedings of the 2nd Conference on Learning for Dynamics and Control, PMLR 120:781-792, 2020.

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 this Paper


BibTeX
@InProceedings{pmlr-v120-khojasteh20a, 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}, pages = {781--792}, year = {2020}, editor = {Alexandre M. Bayen and Ali Jadbabaie and George Pappas and Pablo A. Parrilo and Benjamin Recht and Claire Tomlin and Melanie Zeilinger}, volume = {120}, series = {Proceedings of Machine Learning Research}, address = {The Cloud}, month = {10--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v120/khojasteh20a/khojasteh20a.pdf}, url = {http://proceedings.mlr.press/v120/khojasteh20a.html}, 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.} }
Endnote
%0 Conference Paper %T Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics %A Mohammad Javad Khojasteh %A Vikas Dhiman %A Massimo Franceschetti %A Nikolay Atanasov %B Proceedings of the 2nd Conference on Learning for Dynamics and Control %C Proceedings of Machine Learning Research %D 2020 %E Alexandre M. Bayen %E Ali Jadbabaie %E George Pappas %E Pablo A. Parrilo %E Benjamin Recht %E Claire Tomlin %E Melanie Zeilinger %F pmlr-v120-khojasteh20a %I PMLR %J Proceedings of Machine Learning Research %P 781--792 %U http://proceedings.mlr.press %V 120 %W PMLR %X 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.
APA
Khojasteh, M.J., Dhiman, V., Franceschetti, M. & Atanasov, N.. (2020). Probabilistic Safety Constraints for Learned High Relative Degree System Dynamics. Proceedings of the 2nd Conference on Learning for Dynamics and Control, in PMLR 120:781-792

Related Material