Zobrazeno 1 - 10
of 50
pro vyhledávání: '"Cristian Ioan Vasile"'
Publikováno v:
Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52be4e895069a3fc21a2b5abfb78fb87
https://doi.org/10.1145/3575870.3587120
https://doi.org/10.1145/3575870.3587120
Autor:
Xiao Li, Guy Rosman, Igor Gilitschenski, Brandon Araki, Cristian-Ioan Vasile, Sertac Karaman, Daniela Rus
Publikováno v:
IEEE Robotics and Automation Letters. 7:984-991
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bca6a6a5b6d91e5c36d03ae4fa6dc7a
https://doi.org/10.1109/lra.2021.3135940
https://doi.org/10.1109/lra.2021.3135940
Publikováno v:
IEEE Robotics and Automation Letters. 7:1190-1197
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::23ccfdb53c5f889813135acc25c473e0
https://doi.org/10.1109/lra.2021.3138766
https://doi.org/10.1109/lra.2021.3138766
Publikováno v:
IEEE Robotics and Automation Letters. 7:2297-2304
In this work, we focus on decomposing large multi-agent path planning problems with global temporal logic goals (common to all agents) into smaller sub-problems that can be solved and executed independently. Crucially, the sub-problems' solutions mus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ad7ee3e5bb22417ad9db73f58aa087a7
https://doi.org/10.1109/lra.2022.3143304
https://doi.org/10.1109/lra.2022.3143304
Partial Satisfaction of Signal Temporal Logic Specifications for Coordination of Multi-robot Systems
Publikováno v:
Algorithmic Foundations of Robotics XV ISBN: 9783031210891
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36b7248daa614934e412919da7ae3e02
https://doi.org/10.1007/978-3-031-21090-7_14
https://doi.org/10.1007/978-3-031-21090-7_14
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Time-series data classification is central to the analysis and control of autonomous systems, such as robots and self-driving cars. Temporal logic-based learning algorithms have been proposed recently as classifiers of such data. However, current fra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b3db5aaa24029d2855a6d83dbe589018
https://doi.org/10.1109/iros47612.2022.9982105
https://doi.org/10.1109/iros47612.2022.9982105
Autor:
Kaier Liang, Cristian-Ioan Vasile
Publikováno v:
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
Mobility-on-demand systems are transforming the way we think about the transportation of people and goods. Most research effort has been placed on scalability issues for systems with a large number of agents and simple pick-up/drop-off demands. In th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::920d257fb2c385032ccfde956b719f3d
https://doi.org/10.1109/iros47612.2022.9981291
https://doi.org/10.1109/iros47612.2022.9981291
Autor:
Brandon Araki, Kiran Vodrahalli, Daniela Rus, Thomas Leech, Mark Donahue, Cristian-Ioan Vasile
Publikováno v:
Autonomous Robots. 45:1013-1028
We introduce a method to learn policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achiev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::76e36022c7a2b2c035430110292d7c40
https://doi.org/10.1007/s10514-021-09993-6
https://doi.org/10.1007/s10514-021-09993-6
Publikováno v:
The International Journal of Robotics Research. 39:1002-1028
We develop a sampling-based motion planning algorithm that combines long-term temporal logic goals with short-term reactive requirements. The mission specification has two parts: (1) a global specification given as a linear temporal logic (LTL) formu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::94368ca4f8d62338e201b5852e614791
https://doi.org/10.1177/0278364920918919
https://doi.org/10.1177/0278364920918919
Autor:
Kiran Vodrahalli, Daniela Rus, Mark Donahue, Thomas Leech, Brandon Araki, Cristian-Ioan Vasile
Publikováno v:
AAAI
Other repository
Other repository
We introduce a method to learn imitative policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d5fae9c397bc2ef303528ac6dbda630
https://doi.org/10.1609/aaai.v34i06.6559
https://doi.org/10.1609/aaai.v34i06.6559