Michael Everett

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 61

Top Topics

Reinforcement Learning

Unknown Environments

Motion Planning

Top Venues

IEEE Control. Syst. Lett.

Publications

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 (2024)
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 (2024)
Zihao Dong, Shayegan Omidshafiei, Michael Everett
Collision Avoidance Verification of Multiagent Systems With Learned Policies. IEEE Control. Syst. Lett. 8 (2024)
Zihao Dong, Shayegan Omidshafiei, Michael Everett
Collision Avoidance Verification of Multiagent Systems with Learned Policies. CoRR (2024)
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 (2023)
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 (2023)
Michael Potter, Stefano Maxenti, Michael Everett
Robust Survival Analysis with Adversarial Regularization. CoRR (2023)
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)
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)
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 (2023)
Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops. ACC (2023)
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)
Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map. CoRR (2022)
Nicholas Rober, Michael Everett, Songan Zhang, Jonathan P. How
A Hybrid Partitioning Strategy for Backward Reachability of Neural Feedback Loops. CoRR (2022)
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 (2022)
Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC. ICRA (2022)
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) (2022)
Nicholas Rober, Michael Everett, Jonathan P. How
Backward Reachability Analysis for Neural Feedback Loops. CDC (2022)
Nicholas Rober, Michael Everett, Jonathan P. How
Backward Reachability Analysis for Neural Feedback Loops. CoRR (2022)
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 (2022)
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 (2022)
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 (2022)
Xiaoyi Cai, Michael Everett, Jonathan Fink, Jonathan P. How
Risk-Aware Off-Road Navigation via a Learned Speed Distribution Map. IROS (2022)
Michael Everett, Rudy Bunel, Shayegan Omidshafiei
DRIP: Domain Refinement Iteration with Polytopes for Backward Reachability Analysis of Neural Feedback Loops. CoRR (2022)
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) (2022)
Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How
Reachability Analysis of Neural Feedback Loops. IEEE Access 9 (2021)
Michael Everett, Golnaz Habibi, Jonathan P. How
Robustness Analysis of Neural Networks via Efficient Partitioning with Applications in Control Systems. ACC (2021)
Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora
Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians. CoRR (2021)
Michael Everett, Golnaz Habibi, Jonathan P. How
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers. CoRR (2021)
Michael Everett
Neural Network Verification in Control. CoRR (2021)
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) (2021)
Michael Everett
Neural Network Verification in Control. CDC (2021)
Michael Everett, Golnaz Habibi, Chuangchuang Sun, Jonathan P. How
Reachability Analysis of Neural Feedback Loops. CoRR (2021)
Michael Everett, Golnaz Habibi, Jonathan P. How
Efficient Reachability Analysis of Closed-Loop Systems with Neural Network Controllers. ICRA (2021)
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) (2021)
Michael Everett, Yu Fan Chen, Jonathan P. How
Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning. IEEE Access 9 (2021)
Andrea Tagliabue, Dong-Ki Kim, Michael Everett, Jonathan P. How
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC. CoRR (2021)
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) (2020)
Jesus Tordesillas, Brett Thomas Lopez, Michael Everett, Jonathan P. How
FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments. CoRR (2020)
Rose E. Wang, Michael Everett, Jonathan P. How
R-MADDPG for Partially Observable Environments and Limited Communication. CoRR (2020)
Michael Everett, Björn Lütjens, Jonathan P. How
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRR (2020)
Michael Everett, Golnaz Habibi, Jonathan P. How
Robustness Analysis of Neural Networks via Efficient Partitioning: Theory and Applications in Control Systems. CoRR (2020)
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 (2020)
Michael Everett, Justin Miller, Jonathan P. How
Planning Beyond the Sensing Horizon Using a Learned Context. CoRR (2019)
Björn Lütjens, Michael Everett, Jonathan P. How
Safe Reinforcement Learning With Model Uncertainty Estimates. ICRA (2019)
Michael Everett, Yu Fan Chen, Jonathan P. How
Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning. CoRR (2019)
Björn Lütjens, Michael Everett, Jonathan P. How
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRR (2019)
Björn Lütjens, Michael Everett, Jonathan P. How
Certified Adversarial Robustness for Deep Reinforcement Learning. CoRL (2019)
Michael Everett, Justin Miller, Jonathan P. How
Planning Beyond The Sensing Horizon Using a Learned Context. IROS (2019)
Michael Everett, Yu Fan Chen, Jonathan P. How
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. CoRR (2018)
Björn Lütjens, Michael Everett, Jonathan P. How
Safe Reinforcement Learning with Model Uncertainty Estimates. CoRR (2018)
Sohail Razzaq, Costas Xydeas, Michael Everett, Anzar Mahmood, Thamer Alquthami
Three-Dimensional UAV Routing With Deconfliction. IEEE Access 6 (2018)
Michael Everett, Yu Fan Chen, Jonathan P. How
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning. IROS (2018)
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 (2017)
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 (2017)
Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
Socially Aware Motion Planning with Deep Reinforcement Learning. CoRR (2017)
Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
Socially aware motion planning with deep reinforcement learning. IROS (2017)
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)
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)
Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How
Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning. ICRA (2017)
Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How
Decentralized Non-communicating Multiagent Collision Avoidance with Deep Reinforcement Learning. CoRR (2016)
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)
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)
Plamen P. Angelov, Cosmin Danut Bocaniala, Costas S. Xydeas, Charles Patchett, Daren Ansell, Michael Everett, Gang Leng
A Passive Approach to Autonomous Collision Detection and Avoidance. UKSim (2008)
Rupal Patel, Michael Everett, Eldar Sadikov
Loudmouth: : modifying text-to-speech synthesis in noise. ASSETS (2006)