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2020 – today
- 2024
[j11]Zihao Dong
, Shayegan Omidshafiei, Michael Everett:
Collision Avoidance Verification of Multiagent Systems With Learned Policies. IEEE Control. Syst. Lett. 8: 652-657 (2024)- [j10]Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma, Philip R. Osteen, Bernadette Bucher, Stephen Phillips, Jiuguang Wang, Michael Everett, Nicholas Roy, Jonathan P. How:
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy. IEEE Trans. Robotics 40: 3756-3777 (2024) - [i35]Zihao Dong, Shayegan Omidshafiei, Michael Everett:
Collision Avoidance Verification of Multiagent Systems with Learned Policies. CoRR abs/2403.03314 (2024) - [i34]Michael Potter, Shuo Tang, Paul Ghanem, Milica Stojanovic, Pau Closas, Murat Akçakaya, Ben Wright, Marius Necsoiu, Deniz Erdogmus, Michael Everett, Tales Imbiriba:
Continuously Optimizing Radar Placement with Model Predictive Path Integrals. CoRR abs/2405.18999 (2024) - [i33]Zihao Dong, Jeff Pflueger, Leonard Jung, David Thorne, Philip R. Osteen, Christa S. Robison, Brett Thomas Lopez, Michael Everett:
LiDAR Inertial Odometry And Mapping Using Learned Registration-Relevant Features. CoRR abs/2410.02961 (2024) - [i32]Ananya Trivedi, Sarvesh Prajapati, Mark Zolotas, Michael Everett, Taskin Padir:
Chance-Constrained Convex MPC for Robust Quadruped Locomotion Under Parametric and Additive Uncertainties. CoRR abs/2411.03481 (2024) - [i31]Leonard Jung, Alexander Estornell, Michael Everett:
Contingency Constrained Planning with MPPI within MPPI. CoRR abs/2412.09777 (2024) - 2023
- [j9]Michael Everett, Rudy Bunel, Shayegan Omidshafiei:
DRIP: Domain Refinement Iteration With Polytopes for Backward Reachability Analysis of Neural Feedback Loops. IEEE Control. Syst. Lett. 7: 1622-1627 (2023) - [c22]Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How:
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops. ACC 2023: 3523-3528 - [c21]Lakshay Sharma, Michael Everett, Donggun Lee, Xiaoyi Cai, Philip R. Osteen, Jonathan P. How:
RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation. ICRA 2023: 5730-5736 - [c20]Xiaoyi Cai, Michael Everett, Lakshay Sharma, Philip R. Osteen, Jonathan P. How:
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments. IROS 2023: 11297-11304 - [i30]Anthony G. Francis, Claudia Pérez-D'Arpino, Chengshu Li, Fei Xia, Alexandre Alahi, Rachid Alami, Aniket Bera, Abhijat Biswas, Joydeep Biswas, Rohan Chandra, Hao-Tien Lewis Chiang, Michael Everett, Sehoon Ha, Justin W. Hart, Jonathan P. How, Haresh Karnan, Tsang-Wei Edward Lee, Luis J. Manso, Reuth Mirsky, Sören Pirk, Phani-Teja Singamaneni, Peter Stone, Ada V. Taylor, Peter Trautman, Nathan Tsoi, Marynel Vázquez, Xuesu Xiao, Peng Xu, Naoki Yokoyama, Alexander Toshev, Roberto Martin Martin:
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms. CoRR abs/2306.16740 (2023) - [i29]Xiaoyi Cai, Siddharth Ancha, Lakshay Sharma, Philip R. Osteen, Bernadette Bucher, Stephen Phillips, Jiuguang Wang, Michael Everett, Nicholas Roy, Jonathan P. How:
EVORA: Deep Evidential Traversability Learning for Risk-Aware Off-Road Autonomy. CoRR abs/2311.06234 (2023) - [i28]Michael Potter, Stefano Maxenti, Michael Everett:
Robust Survival Analysis with Adversarial Regularization. CoRR abs/2312.16019 (2023) - 2022
- [j8]Michael Everett, Björn Lütjens, Jonathan P. How:
Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4184-4198 (2022) - [j7]Jesus Tordesillas, Brett Thomas Lopez, Michael Everett, Jonathan P. How:
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments. IEEE Trans. Robotics 38(2): 922-938 (2022) - [c19]Nicholas Rober, Michael Everett, Jonathan P. How:
Backward Reachability Analysis for Neural Feedback Loops. CDC 2022: 2897-2904 - [c18]Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How:
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC. ICRA 2022: 462-468 - [c17]Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How:
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map. IROS 2022: 2931-2937 - [c16]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. NeurIPS 2022 - [i27]Dong-Ki Kim, Matthew Riemer, Miao Liu, Jakob N. Foerster, Michael Everett, Chuangchuang Sun, Gerald Tesauro, Jonathan P. How:
Influencing Long-Term Behavior in Multiagent Reinforcement Learning. CoRR abs/2203.03535 (2022) - [i26]Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How:
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map. CoRR abs/2203.13429 (2022) - [i25]Nicholas Rober, Michael Everett, Jonathan P. How:
Backward Reachability Analysis for Neural Feedback Loops. CoRR abs/2204.08319 (2022) - [i24]Nicholas Rober, Sydney M. Katz, Chelsea Sidrane, Esen Yel, Michael Everett, Mykel J. Kochenderfer, Jonathan P. How:
Backward Reachability Analysis of Neural Feedback Loops: Techniques for Linear and Nonlinear Systems. CoRR abs/2209.14076 (2022) - [i23]Xiaoyi Cai, Michael Everett, Lakshay Sharma, Philip R. Osteen, Jonathan P. How:
Probabilistic Traversability Model for Risk-Aware Motion Planning in Off-Road Environments. CoRR abs/2210.00153 (2022) - [i22]Lakshay Sharma, Michael Everett, Donggun Lee, Xiaoyi Cai, Philip R. Osteen, Jonathan P. How:
RAMP: A Risk-Aware Mapping and Planning Pipeline for Fast Off-Road Ground Robot Navigation. CoRR abs/2210.06605 (2022) - [i21]Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How:
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops. CoRR abs/2210.07918 (2022) - [i20]Michael Everett, Rudy Bunel, Shayegan Omidshafiei:
DRIP: Domain Refinement Iteration with Polytopes for Backward Reachability Analysis of Neural Feedback Loops. CoRR abs/2212.04646 (2022) - 2021
- [j6]Michael Everett, Yu Fan Chen, Jonathan P. How:
Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning. IEEE Access 9: 10357-10377 (2021) - [j5]Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How:
Reachability Analysis of Neural Feedback Loops. IEEE Access 9: 163938-163953 (2021) - [j4]Michael Everett, Golnaz Habibi, Jonathan P. How:
Robustness Analysis of Neural Networks via Efficient Partitioning With Applications in Control Systems. IEEE Control. Syst. Lett. 5(6): 2114-2119 (2021) - [j3]Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora:
Where to go Next: Learning a Subgoal Recommendation Policy for Navigation in Dynamic Environments. IEEE Robotics Autom. Lett. 6(3): 4616-4623 (2021) - [c15]Michael Everett, Golnaz Habibi, Jonathan P. How:
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems. ACC 2021: 888-893 - [c14]Michael Everett:
Neural Network Verification in Control. CDC 2021: 6326-6340 - [c13]Michael Everett, Golnaz Habibi, Jonathan P. How:
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers. ICRA 2021: 4384-4390 - [i19]Michael Everett, Golnaz Habibi, Jonathan P. How:
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers. CoRR abs/2101.01815 (2021) - [i18]Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora:
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians. CoRR abs/2102.13073 (2021) - [i17]Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How:
Reachability Analysis of Neural Feedback Loops. CoRR abs/2108.04140 (2021) - [i16]Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How:
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC. CoRR abs/2109.09910 (2021) - [i15]Michael Everett:
Neural Network Verification in Control. CoRR abs/2110.01388 (2021) - 2020
- [j2]Samaneh Hosseini Semnani, Hugh Liu, Michael Everett, Anton H. J. de Ruiter, Jonathan P. How:
Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning. IEEE Robotics Autom. Lett. 5(2): 3221-3226 (2020) - [i14]Jesus Tordesillas, Brett Thomas Lopez, Michael Everett, Jonathan P. How:
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. CoRR abs/2001.04420 (2020) - [i13]Samaneh Hosseini Semnani, Hugh Liu, Michael Everett, Anton H. J. de Ruiter, Jonathan P. How:
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning. CoRR abs/2001.06627 (2020) - [i12]Rose E. Wang, Michael Everett, Jonathan P. How:
R-MADDPG for Partially Observable Environments and Limited Communication. CoRR abs/2002.06684 (2020) - [i11]Michael Everett, Björn Lütjens, Jonathan P. How:
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRR abs/2004.06496 (2020) - [i10]Michael Everett, Golnaz Habibi, Jonathan P. How:
Robustness Analysis of Neural Networks via Efficient Partitioning: Theory and Applications in Control Systems. CoRR abs/2010.00540 (2020)
2010 – 2019
- 2019
- [c12]Björn Lütjens, Michael Everett, Jonathan P. How:
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRL 2019: 1328-1337 - [c11]Björn Lütjens, Michael Everett, Jonathan P. How:
Safe Reinforcement Learning With Model Uncertainty Estimates. ICRA 2019: 8662-8668 - [c10]Michael Everett, Justin Miller, Jonathan P. How:
Planning Beyond The Sensing Horizon Using a Learned Context. IROS 2019: 1064-1071 - [i9]Michael Everett, Justin Miller, Jonathan P. How:
Planning Beyond the Sensing Horizon Using a Learned Context. CoRR abs/1908.09171 (2019) - [i8]Michael Everett, Yu Fan Chen, Jonathan P. How:
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning. CoRR abs/1910.11689 (2019) - [i7]Björn Lütjens, Michael Everett, Jonathan P. How:
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRR abs/1910.12908 (2019) - 2018
- [j1]Sohail Razzaq, Costas Xydeas, Michael Everett, Anzar Mahmood, Thamer Alquthami:
Three-Dimensional UAV Routing With Deconfliction. IEEE Access 6: 21536-21551 (2018) - [c9]Michael Everett, Yu Fan Chen, Jonathan P. How:
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. IROS 2018: 3052-3059 - [i6]Michael Everett, Yu Fan Chen, Jonathan P. How:
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. CoRR abs/1805.01956 (2018) - [i5]Björn Lütjens, Michael Everett, Jonathan P. How:
Safe Reinforcement Learning with Model Uncertainty Estimates. CoRR abs/1810.08700 (2018) - 2017
- [c8]Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How:
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. ICRA 2017: 285-292 - [c7]Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian:
Scalable accelerated decentralized multi-robot policy search in continuous observation spaces. ICRA 2017: 863-870 - [c6]Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett Thomas Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian:
Semantic-level decentralized multi-robot decision-making using probabilistic macro-observations. ICRA 2017: 871-878 - [c5]Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How:
Socially aware motion planning with deep reinforcement learning. IROS 2017: 1343-1350 - [i4]Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett Thomas Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian:
Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations. CoRR abs/1703.05623 (2017) - [i3]Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P. How, John Vian:
Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces. CoRR abs/1703.05626 (2017) - [i2]Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How:
Socially Aware Motion Planning with Deep Reinforcement Learning. CoRR abs/1703.08862 (2017) - 2016
- [i1]Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How:
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning. CoRR abs/1609.07845 (2016) - 2014
- [c4]Hisham Bedri, Micha Feigin, Michael Everett, Ivan Filho, Gregory L. Charvat, Ramesh Raskar:
Seeing around corners with a mobile phone?: synthetic aperture audio imaging. SIGGRAPH Posters 2014: 84:1 - 2013
- [c3]Titus Barik, Michael Everett, Rogelio Enrique Cardona-Rivera, David L. Roberts, Edward F. Gehringer:
A community college blended learning classroom experience through Artificial Intelligence in Games. FIE 2013: 1525-1531
2000 – 2009
- 2008
- [c2]Plamen Angelov, Cosmin Danut Bocaniala, Costas S. Xydeas, Charles Patchett, Darren Ansell, Michael Everett, Gang Leng:
A Passive Approach to Autonomous Collision Detection and Avoidance. UKSim 2008: 64-69 - 2006
- [c1]Rupal Patel, Michael Everett, Eldar Sadikov:
Loudmouth: : modifying text-to-speech synthesis in noise. ASSETS 2006: 227-228
Coauthor Index
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