5 papers
Variational Inference via Entropic Transport Descent
Vincent Pacelli, Akash Ratheesh, Evangelos Theodorou
Particle-based variational inference (ParVI) methods approximate an intractable target distribution by evolving an ensemble of interacting samples. Existing approaches rely predomi…
Beyond Pure Sampling: Hybrid Optimization Mechanisms for Non-Convex Model Predictive Control
Yuichiro Aoyama, Minchan Jung, Akash Ratheesh +1
This paper investigates the optimization mechanisms of non-convex Model Predictive Control (MPC) using the Maximum Entropy Differential Dynamic Programming (ME-DDP) framework. Navi…
Sampling-Based Control via Entropy-Regularized Optimal Transport
Vincent Pacelli, Akash Ratheesh, Evangelos A. Theodorou
Sampling-based model predictive control methods like MPPI and CEM are essential for real-time control of nonlinear robotic systems, particularly where discontinuous dynamics preclu…
Operator Splitting Covariance Steering for Safe Stochastic Nonlinear Control
Akash Ratheesh, Vincent Pacelli, Augustinos D. Saravanos +1
This paper presents a novel algorithm for solving distribution steering problems featuring nonlinear dynamics and chance constraints. Covariance steering (CS) is an emerging method…
Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions
Rayan Mazouz, Karan Muvvala, Akash Ratheesh +2
Neural Networks (NNs) have been successfully employed to represent the state evolution of complex dynamical systems. Such models, referred to as NN dynamic models (NNDMs), use iter…