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Michael Everett
Michael Everett
Assistant Professor, Northeastern University
Bestätigte E-Mail-Adresse bei mit.edu - Startseite
Titel
Zitiert von
Zitiert von
Jahr
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
8142017
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
7272017
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
5952018
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
278*2021
Collision avoidance in pedestrian-rich environments with deep reinforcement learning
M Everett, YF Chen, JP How
Ieee Access 9, 10357-10377, 2021
1992021
Safe reinforcement learning with model uncertainty estimates
B Lütjens, M Everett, JP How
2019 International Conference on Robotics and Automation (ICRA), 8662-8668, 2019
1882019
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
1222020
Certified Adversarial Robustness for Deep Reinforcement Learning
B Lütjens, M Everett, JP How
2019 Conference on Robot Learning (CoRL), 2019
922019
R-MADDPG for partially observable environments and limited communication
RE Wang, M Everett, JP How
arXiv preprint arXiv:2002.06684, 2020
872020
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
732021
Certified adversarial robustness for deep reinforcement learning
M Everett, B Lutjens, JP How
arXiv preprint arXiv:2004.06496, 2020
60*2020
Reachability analysis of neural feedback loops
M Everett, G Habibi, C Sun, JP How
IEEE Access 9, 163938-163953, 2021
572021
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
452023
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
312022
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
262020
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
232023
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
232022
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
212023
Efficient reachability analysis of closed-loop systems with neural network controllers
M Everett, G Habibi, JP How
2021 IEEE International Conference on Robotics and Automation (ICRA), 4384-4390, 2021
202021
Planning Beyond the Sensing Horizon Using a Learned Context
M Everett, J Miller, JP How
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019
192019
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