Aerospace Controls Laboratory

Michael Everett


GitHub  /  Website
mfe [at] mit [dot] edu


  • Ph.D. in Mechanical Engineering, MIT, 2020
  • S.M. in Mechanical Engineering, MIT, 2017
  • S.B. in Mechanical Engineering, MIT, 2015

Research Interests

  • Robust Learning
  • Collision Avoidance
  • Motion Planning


Backward Reachability for Neural Feedback Loops
Nicholas Rober, Michael Everett, 2022

This project developed a backward reachability strategy to certify safety for systems controlled by neural networks

Efficient Learning of Neural Network Policies via Imitation Learning and Tube MPC
Andrea Tagliabue, Dong-Ki Kim, Michael Everett, 2022

Use a Robust Tube variant of MPC to efficiently learn Neural Network policies via Imitation Learning.

Risk-Aware Off-Road Navigation Leveraging Semantics
Xiaoyi (Jeremy) Cai, Michael Everett, 2022

Use semantics of the environment to infer terrain traversability based on history of speed data.

Certified Adversarial Robustness for Deep RL
Michael Everett, Björn Lütjens, 2020

This project develops deep RL algorithms that are robust to an adversarial perturbation in the observation space

Self-Driving Delivery Robot
Michael Everett, Justin Miller (Ford), 2019

This project develops planning algorithms to enable autonomous navigation in the "last 100m" for delivery robots

Socially Acceptable Navigation
Michael Everett, Steven Chen, 2019

Collision avoidance algorithm using Deep RL.

Robust and Interpretable RL for Navigation in Pedestrian Crowds
Björn Lütjens, Michael Everett, 2018

Deep neural networks can fail overconfidently on novel observations. This work pioneers a reinforcement learning framework that reasons about the predictive confidence and is more robust to novel observations.