Michael Everett

mfe@mit.edu
31-235C
Google Scholar
GitHub
YouTube Playlist

I am a Postdoctoral Associate at MIT in the Aerospace Controls Lab. My research interests lie at the intersection of robotics and learning, with an emphasis on robust learning, motion planning and deep reinforcement learning. My PhD work was advised by Prof. Jonathan How, Prof. John Leonard, and Prof. Alberto Rodriguez. I received the PhD, SM, and SB degrees from MIT in Mechanical Engineering (2020, 2017, 2015).


Today’s robots are designed for humans, but are rarely deployed among humans. My PhD thesis addressed problems of perception, planning, and safety that arise when deploying a mobile robot in human environments.


Selected Awards

  • Winner: Best Paper Award on Cognitive Robotics (IROS 2019)
  • Winner: Best Student Paper (IROS 2017)
  • Finalist: Best Paper Award on Cognitive Robotics (IROS 2017)
  • Finalist: Best Multi-Robot Systems Paper (ICRA 2017)
  • iCampus Student Prize Winner ($3,000) for ofcourse.mit.edu

Publications


Pre-prints

Certified Adversarial Robustness for Deep Reinforcement Learning

Michael Everett*, Björn Lütjens*, Jonathan P. How
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in review
Paper

Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning

Michael Everett, Yu Fan Chen, Jonathan P. How
Intl. Journal of Robotics Research (IJRR), in review
Paper     Code: [ Pre-Trained ROS Package , Training Environment , Training Code (coming soon...) ]

FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments

Jesus Tordesillas Torres, Brett T. Lopez, Michael Everett, Jonathan P. How
IEEE Transactions on Robotics (TRO), in review
Paper


2020

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

Samaneh Hoseini, Hugh H.T. Liu, Michael Everett, Anton de Ruiter, Jonathan P. How
IEEE Robotics & Automation Letters (RA-L)
Vol. 5, No. 2, pp. 3221-3226, April 2020
Paper


2019

Planning Beyond The Sensing Horizon Using a Learned Context

Michael Everett, Justin Miller, Jonathan P. How
IROS 2019
Winner: Best Paper Award on Cognitive Robotics
Paper     Code     Video

Certified Adversarial Robustness in Deep Reinforcement Learning

Björn Lütjens, Michael Everett, Jonathan P. How
Conference on Robot Learning (CoRL) 2019
Paper     Talk

R-MADDPG for Partially Observable Environments and Limited Communication

Rose E Wang, Michael Everett, Jonathan P How
Reinforcement Learning for Real Life (RL4RealLife) Workshop in ICML 2019
Paper     Code

Safe Reinforcement Learning with Model Uncertainty Estimates

Björn Lütjens, Michael Everett, Jonathan P. How
ICRA 2019
Selected for Oral Presentation at IROS 2018 Workshop on Machine Learning in Motion Planning
Paper

2018

Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning

Michael Everett, Yu Fan Chen, Jonathan P. How
IROS 2018
Paper     Code     Video

2017

Socially Aware Motion Planning With Deep Reinforcement Learning

Yu Fan Chen, Michael Everett, Miao Liu, Jonathan P. How
IROS 2017
Winner: Best Student Paper Award
Finalist: Best Paper Award on Cognitive Robotics
Paper     Video

Decentralized Non-Communicating Multiagent Collision Avoidance With Deep Reinforcement Learning

Yu Fan Chen, Miao Liu, Michael Everett, Jonathan P. How
ICRA 2017
Finalist: Best Multi-Robot Systems Paper
Paper     Video

Scalable Accelerated Decentralized Multi-Robot Policy Search in Continuous Observation Spaces

Shayegan Omidshafiei, Christopher Amato, Miao Liu, Michael Everett, Jonathan P How, and John Vian
ICRA 2017
Paper    

Semantic-level Decentralized Multi-Robot Decision-Making using Probabilistic Macro-Observations

Shayegan Omidshafiei, Shih-Yuan Liu, Michael Everett, Brett T. Lopez, Christopher Amato, Miao Liu, Jonathan P. How, John Vian
ICRA 2017
Paper     Video

2014

Seeing around corners with a mobile phone? Synthetic aperture audio imaging

Hisham Bedri, Micha Feigin, Michael Everett, Ivan Filho, Gregory L. Charvat, Ramesh Raskar
ACM SIGGRAPH 2014 Posters
Paper

Theses

Algorithms for Robust Autonomous Navigation in Human Environments

Michael Everett
PhD Thesis, MIT
Paper     Defense Talk

Robot Designed for Socially Acceptable Navigation

Michael Everett
SM Thesis, MIT
Paper

Recent & Ongoing Research Projects



Safety & Robustness in Deep RL

Self-Driving Delivery Robot



Socially Acceptable Navigation

Quadrotor Safety System






Featured Press




Open-Source Software



CADRL ROS package


Star Fork

Loomo Android-ROS


Star Fork

Deep Cost-to-Go


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Trajectory Overlays


Star Fork


Collision Avoidance Training Environment (Gym)


Star Fork