6 papers
A Compression Perspective on Simplicity Bias
Tom Marty, Eric Elmoznino, Leo Gagnon +5
Deep neural networks exhibit a simplicity bias, a well-documented tendency to favor simple functions over complex ones. In this work, we cast new light on this phenomenon through t…
Optimal Power Management of Battery Energy Storage Systems via Ensemble Kalman Inversion
Amir Farakhor, Iman Askari, Di Wu +1
Optimal power management of battery energy storage systems (BESS) is crucial for their safe and efficient operation. Numerical optimization techniques are frequently utilized to so…
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
Iman Askari, Yebin Wang, Vedeng M. Deshpande +1
Safe and efficient motion planning is of fundamental importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model predictive control (NMPC)…
Optimal Power Management for Large-Scale Battery Energy Storage Systems via Bayesian Inference
Amir Farakhor, Iman Askari, Di Wu +2
Large-scale battery energy storage systems (BESS) have found ever-increasing use across industry and society to accelerate clean energy transition and improve energy supply reliabi…
Bayesian Inferential Motion Planning Using Heavy-Tailed Distributions
Ali Vaziri, Iman Askari, Huazhen Fang
Robots rely on motion planning to navigate safely and efficiently while performing various tasks. In this paper, we investigate motion planning through Bayesian inference, where mo…
MultiNash-PF: A Particle Filtering Approach for Computing Multiple Local Generalized Nash Equilibria in Trajectory Games
Maulik Bhatt, Iman Askari, Yue Yu +3
Modern robotic systems frequently engage in complex multi-agent interactions, many of which are inherently multi-modal, i.e., they can lead to multiple distinct outcomes. To intera…