Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Franck Djeumou"'
Publikováno v:
IEEE Transactions on Automatic Control. 68:2245-2260
We develop a probabilistic control algorithm, $\texttt{GTLProCo}$, for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The resulting algorithm not only achieves decentralized control of the swar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::851ff1d3ca9bb8f277bed526d66183e4
https://doi.org/10.1109/tac.2022.3176797
https://doi.org/10.1109/tac.2022.3176797
Publikováno v:
IEEE Transactions on Automatic Control. :1-16
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c71ba68afb5dcfad38e88b70413450db
https://doi.org/10.1109/tac.2022.3217260
https://doi.org/10.1109/tac.2022.3217260
Publikováno v:
IEEE Transactions on Control of Network Systems. 8:621-632
We study the problem of enforcing safety in multiagent systems at runtime by modifying the system behavior if a potential safety violation is detected. Traditional runtime enforcement methods that solve a reactive synthesis problem at design time hav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bf78b180adeb917a962ed96b75e239f4
https://doi.org/10.1109/tcns.2021.3061900
https://doi.org/10.1109/tcns.2021.3061900
Publikováno v:
Artificial Intelligence. 317:103856
In inverse reinforcement learning (IRL), a learning agent infers a reward function encoding the underlying task using demonstrations from experts. However, many existing IRL techniques make the often unrealistic assumption that the agent has access t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::13b4cd7bab034ca5b18263afd048a614
https://doi.org/10.1016/j.artint.2023.103856
https://doi.org/10.1016/j.artint.2023.103856
Neural ordinary differential equations (NODEs) -- parametrizations of differential equations using neural networks -- have shown tremendous promise in learning models of unknown continuous-time dynamical systems from data. However, every forward eval
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb482115059e0ecec5f563e59b56436d
Publikováno v:
2021 60th IEEE Conference on Decision and Control (CDC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5deab85ebd76ed023c1ba4b5ae8291cd
https://doi.org/10.1109/cdc45484.2021.9682945
https://doi.org/10.1109/cdc45484.2021.9682945
Publikováno v:
HSCC
We describe data-driven algorithms, DaTaReach and DaTaControl, for reachability analysis and control of systems with a priori unknown nonlinear dynamics. The resulting algorithms provide provable performance guarantees while satisfying real-time cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6dc7818bba29f902ee4560ff066b2488
https://doi.org/10.1145/3447928.3457355
https://doi.org/10.1145/3447928.3457355
Publikováno v:
Robotics: Science and Systems
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4334439b3ef31161a828dca163c560f6
https://doi.org/10.15607/rss.2020.xvi.058
https://doi.org/10.15607/rss.2020.xvi.058
We study the synthesis of mode switching protocols for a class of discrete-time switched linear systems in which the mode jumps are governed by Markov decision processes (MDPs). We call such systems MDP-JLS for brevity. Each state of the MDP correspo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::49e0c5c5faeae41523468ded1f0aedf1
Safety and performance are often two competing objectives in sequential decision-making problems. Existing performant controllers, such as controllers derived from reinforcement learning algorithms, often fall short of safety guarantees. On the contr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8fa1e0273b510af57b001a19663a19ca