Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Adam J. Thorpe"'
Publikováno v:
IEEE Transactions on Control Systems Technology. 30:71-84
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c96c736a7c0e4ad414473193ece64a2
https://doi.org/10.1109/tcst.2021.3056242
https://doi.org/10.1109/tcst.2021.3056242
Variability in human response creates non-trivial challenges for modeling and control of human-automation systems. As autonomy becomes pervasive, methods that can accommodate human variability will become paramount, to ensure efficiency, safety, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d05837399bfee0679e5af0f7ed410748
http://arxiv.org/abs/2206.01331
http://arxiv.org/abs/2206.01331
Autor:
Adam J. Thorpe, Meeko M. K. Oishi
Publikováno v:
IEEE Control Systems Letters. 4:512-517
We present a solution to the terminal-hitting stochastic reach-avoid problem for a Markov control process. This solution takes advantage of a nonparametric representation of the stochastic kernel as a conditional distribution embedding within a repro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce08e2a8f1dd1a9afd77bd5d4cdcb349
https://doi.org/10.1109/lcsys.2019.2954102
https://doi.org/10.1109/lcsys.2019.2954102
Publikováno v:
ACC
We present a method to compute the stochastic reachability safety probabilities for high-dimensional stochastic dynamical systems. Our approach takes advantage of a nonparametric learning technique known as conditional distribution embeddings to mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1b4b2669923e8828e048c062b1c9796
https://doi.org/10.23919/acc50511.2021.9483404
https://doi.org/10.23919/acc50511.2021.9483404
Autor:
Adam J. Thorpe, Meeko M. K. Oishi
Kernel embeddings of distributions have recently gained significant attention in the machine learning community as a data-driven technique for representing probability distributions. Broadly, these techniques enable efficient computation of expectati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48768c0e986243f5eefa0746239d3ff0
State-Based Confidence Bounds for Data-Driven Stochastic Reachability Using Hilbert Space Embeddings
In this paper, we compute finite sample bounds for data-driven approximations of the solution to stochastic reachability problems. Our approach uses a nonparametric technique known as kernel distribution embeddings, and provides probabilistic assuran
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c83c3a01ed5822f714a058e9df6c7045
http://arxiv.org/abs/2010.08036
http://arxiv.org/abs/2010.08036
Publikováno v:
ECS Meeting Abstracts. :857-857
Additively manufactured plating enclosures can be used as a cheap alternative to traditional plating tanks and fixturing, which can reduce the volume of chemistry required, allow for complex fluid dynamics across the part surface, and offer the possi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d4f30fdd21b7eb7e1d4a2962bb4aae3
https://doi.org/10.1149/ma2018-02/24/857
https://doi.org/10.1149/ma2018-02/24/857