8 citations · 8 across the 6 of their papers we have counts for
6 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…
Using Surprise Index for Competency Assessment in Autonomous Decision-Making
Akash Ratheesh, Ofer Dagan, Nisar R. Ahmed +1
This paper considers the problem of evaluating an autonomous system's competency in performing a task, particularly when working in dynamic and uncertain environments. The inherent…
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…