3 citations · 8 across the 5 of their papers we have counts for
6 papers
Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control
Aaron Havens, Darioush Keivan, Peter Seiler +2
Many existing region-of-attraction (ROA) analysis tools find difficulty in addressing feedback systems with large-scale neural network (NN) policies and/or high-dimensional sensing…
Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems
Aaron Havens, Girish Chowdhary
As deep learning becomes more prevalent for prediction and control of real physical systems, it is important that these overparameterized models are consistent with physically plau…
On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions
Aaron Havens, Bin Hu
When applying imitation learning techniques to fit a policy from expert demonstrations, one can take advantage of prior stability/robustness assumptions on the expert's policy and…
Learning Latent State Spaces for Planning through Reward Prediction
Aaron Havens, Yi Ouyang, Prabhat Nagarajan +1
Model-based reinforcement learning methods typically learn models for high-dimensional state spaces by aiming to reconstruct and predict the original observations. However, drawing…
Learning to Cope with Adversarial Attacks
Xian Yeow Lee, Aaron Havens, Girish Chowdhary +1
The security of Deep Reinforcement Learning (Deep RL) algorithms deployed in real life applications are of a primary concern. In particular, the robustness of RL agents in cyber-ph…
Online Robust Policy Learning in the Presence of Unknown Adversaries
Aaron J. Havens, Zhanhong Jiang, Soumik Sarkar
The growing prospect of deep reinforcement learning (DRL) being used in cyber-physical systems has raised concerns around safety and robustness of autonomous agents. Recent work on…