Search Results for author: Aaron Havens

Found 9 papers, 1 papers with code

Capabilities of Large Language Models in Control Engineering: A Benchmark Study on GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra

no code implementations4 Apr 2024 Darioush Kevian, Usman Syed, Xingang Guo, Aaron Havens, Geir Dullerud, Peter Seiler, Lianhui Qin, Bin Hu

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1. 0 Ultra in solving undergraduate-level control problems.

Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations

no code implementations25 Jan 2024 Patricia Pauli, Aaron Havens, Alexandre Araujo, Siddharth Garg, Farshad Khorrami, Frank Allgöwer, Bin Hu

However, a direct application of LipSDP to the resultant residual ReLU networks is conservative and even fails in recovering the well-known fact that the MaxMin activation is 1-Lipschitz.

Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control

no code implementations3 Jan 2022 Aaron Havens, Darioush Keivan, Peter Seiler, Geir Dullerud, Bin Hu

We show that the ROA analysis can be approximated as a constrained maximization problem whose goal is to find the worst-case initial condition which shifts the terminal state the most.

Model-Free $μ$ Synthesis via Adversarial Reinforcement Learning

no code implementations30 Nov 2021 Darioush Keivan, Aaron Havens, Peter Seiler, Geir Dullerud, Bin Hu

We build a connection between robust adversarial RL and $\mu$ synthesis, and develop a model-free version of the well-known $DK$-iteration for solving state-feedback $\mu$ synthesis with static $D$-scaling.

reinforcement-learning Reinforcement Learning (RL)

Forced Variational Integrator Networks for Prediction and Control of Mechanical Systems

no code implementations5 Jun 2021 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 plausible dynamics.

Inductive Bias

On Imitation Learning of Linear Control Policies: Enforcing Stability and Robustness Constraints via LMI Conditions

no code implementations24 Mar 2021 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 incorporate such control-theoretic prior knowledge explicitly into the learning process.

Imitation Learning

Learning Latent State Spaces for Planning through Reward Prediction

no code implementations9 Dec 2019 Aaron Havens, Yi Ouyang, Prabhat Nagarajan, Yasuhiro Fujita

The latent representation is learned exclusively from multi-step reward prediction which we show to be the only necessary information for successful planning.

Model-based Reinforcement Learning reinforcement-learning +1

Learning to Cope with Adversarial Attacks

no code implementations28 Jun 2019 Xian Yeow Lee, Aaron Havens, Girish Chowdhary, Soumik Sarkar

Hence, it is imperative that RL agents deployed in real-life applications have the capability to detect and mitigate adversarial attacks in an online fashion.

Decision Making Meta-Learning

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