64 citations · 117 across the 4 of their papers we have counts for
6 papers
Neural Network Verification in Control
Michael Everett
Learning-based methods could provide solutions to many of the long-standing challenges in control. However, the neural networks (NNs) commonly used in modern learning approaches pr…
Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC
Andrea Tagliabue, Dong-Ki Kim, Michael Everett +1
We propose a demonstration-efficient strategy to compress a computationally expensive Model Predictive Controller (MPC) into a more computationally efficient representation based o…
Reachability Analysis of Neural Feedback Loops
Michael Everett, Golnaz Habibi, Chuangchuang Sun +1
Neural Networks (NNs) can provide major empirical performance improvements for closed-loop systems, but they also introduce challenges in formally analyzing those systems' safety p…
R-MADDPG for Partially Observable Environments and Limited Communication
Rose E. Wang, Michael Everett, Jonathan P. How
There are several real-world tasks that would benefit from applying multiagent reinforcement learning (MARL) algorithms, including the coordination among self-driving cars. The rea…
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
Jesus Tordesillas, Brett T. Lopez, Michael Everett +1
Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the…
Planning Beyond the Sensing Horizon Using a Learned Context
Michael Everett, Justin Miller, Jonathan P. How
Last-mile delivery systems commonly propose the use of autonomous robotic vehicles to increase scalability and efficiency. The economic inefficiency of collecting accurate prior ma…