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Michael Everett
Michael Everett
Assistant Professor, Northeastern University
Email verificata su mit.edu - Home page
Titolo
Citata da
Citata da
Anno
Socially aware motion planning with deep reinforcement learning
YF Chen, M Everett, M Liu, JP How
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017
8622017
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning
YF Chen, M Liu, M Everett, JP How
2017 IEEE international conference on robotics and automation (ICRA), 285-292, 2017
7962017
Motion planning among dynamic, decision-making agents with deep reinforcement learning
M Everett, YF Chen, JP How
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018
6622018
Faster: Fast and safe trajectory planner for navigation in unknown environments
J Tordesillas, BT Lopez, M Everett, JP How
IEEE Transactions on Robotics 38 (2), 922-938, 2021
337*2021
Collision avoidance in pedestrian-rich environments with deep reinforcement learning
M Everett, YF Chen, JP How
Ieee Access 9, 10357-10377, 2021
2322021
Safe reinforcement learning with model uncertainty estimates
B Lütjens, M Everett, JP How
2019 International Conference on Robotics and Automation (ICRA), 8662-8668, 2019
2272019
Multi-agent motion planning for dense and dynamic environments via deep reinforcement learning
SH Semnani, H Liu, M Everett, A De Ruiter, JP How
IEEE Robotics and Automation Letters 5 (2), 3221-3226, 2020
1482020
Certified Adversarial Robustness for Deep Reinforcement Learning
B Lütjens, M Everett, JP How
2019 Conference on Robot Learning (CoRL), 2019
1052019
R-MADDPG for partially observable environments and limited communication
RE Wang, M Everett, JP How
arXiv preprint arXiv:2002.06684, 2020
952020
Where to go next: Learning a subgoal recommendation policy for navigation in dynamic environments
B Brito, M Everett, JP How, J Alonso-Mora
IEEE Robotics and Automation Letters 6 (3), 4616-4623, 2021
922021
Principles and guidelines for evaluating social robot navigation algorithms
A Francis, C Pérez-d'Arpino, C Li, F Xia, A Alahi, R Alami, A Bera, ...
arXiv preprint arXiv:2306.16740, 2023
702023
Certified adversarial robustness for deep reinforcement learning
M Everett, B Lutjens, JP How
arXiv preprint arXiv:2004.06496, 2020
68*2020
Reachability analysis of neural feedback loops
M Everett, G Habibi, C Sun, JP How
IEEE Access 9, 163938-163953, 2021
642021
Risk-aware off-road navigation via a learned speed distribution map
X Cai, M Everett, J Fink, JP How
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022
462022
Probabilistic traversability model for risk-aware motion planning in off-road environments
X Cai, M Everett, L Sharma, PR Osteen, JP How
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023
352023
Evora: Deep evidential traversability learning for risk-aware off-road autonomy
X Cai, S Ancha, L Sharma, PR Osteen, B Bucher, S Phillips, J Wang, ...
IEEE Transactions on Robotics, 2024
332024
Robustness analysis of neural networks via efficient partitioning with applications in control systems
M Everett, G Habibi, JP How
IEEE Control Systems Letters 5 (6), 2114-2119, 2020
302020
Efficient guided policy search via imitation of robust tube MPC
A Tagliabue, DK Kim, M Everett, JP How
2022 International Conference on Robotics and Automation (ICRA), 462-468, 2022
282022
Influencing long-term behavior in multiagent reinforcement learning
DK Kim, M Riemer, M Liu, J Foerster, M Everett, C Sun, G Tesauro, ...
Advances in Neural Information Processing Systems 35, 18808-18821, 2022
242022
Backward reachability analysis of neural feedback loops: Techniques for linear and nonlinear systems
N Rober, SM Katz, C Sidrane, E Yel, M Everett, MJ Kochenderfer, JP How
IEEE Open Journal of Control Systems 2, 108-124, 2023
232023
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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