no code implementations • 10 Apr 2024 • Anant A. Joshi, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn
In this paper, stochastic optimal control problems in continuous time and space are considered.
no code implementations • 2 Feb 2024 • Tixian Wang, Udit Halder, Ekaterina Gribkova, Rhanor Gillette, Mattia Gazzola, Prashant G. Mehta
In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented.
no code implementations • 3 Jan 2023 • Amirhossein Taghvaei, Prashant G. Mehta
In this survey, we describe controlled interacting particle systems (CIPS) to approximate the solution of the optimal filtering and the optimal control problems.
no code implementations • 12 Nov 2022 • Tixian Wang, Udit Halder, Ekaterina Gribkova, Mattia Gazzola, Prashant G. Mehta
The octopus arm is a neuromechanical system that involves a complex interplay between peripheral nervous system (PNS) and arm musculature.
no code implementations • 9 Sep 2022 • Heng-Sheng Chang, Udit Halder, Chia-Hsien Shih, Noel Naughton, Mattia Gazzola, Prashant G. Mehta
Key contributions of this paper are: (i) modeling of major muscle groups to elicit three-dimensional movements; (ii) a mathematical formulation for muscle activations based on a stored energy function; and (iii) a computationally efficient procedure to design task-specific equilibrium configurations, obtained by solving an optimization problem in the Special Euclidean group SE(3).
no code implementations • 1 Apr 2022 • Tixian Wang, Udit Halder, Ekaterina Gribkova, Rhanor Gillette, Mattia Gazzola, Prashant G. Mehta
The main contribution of this paper is a novel sensory feedback control law for an octopus arm.
no code implementations • 2 Jul 2021 • Anant Joshi, Amirhossein Taghvaei, Prashant G. Mehta, Sean P. Meyn
This paper is concerned with optimal control problems for control systems in continuous time, and interacting particle system methods designed to construct approximate control solutions.
no code implementations • 2 Oct 2020 • S. Yagiz Olmez, Amirhossein Taghvaei, Prashant G. Mehta
The exact gain function is the solution of a Poisson equation involving a probability-weighted Laplacian.
no code implementations • 8 Aug 2020 • Prashant G. Mehta, Sean P. Meyn
It is shown that in fact the algorithms are very different: while convex Q-learning solves a convex program that approximates the Bellman equation, theory for DQN is no stronger than for Watkins' algorithm with function approximation: (a) it is shown that both seek solutions to the same fixed point equation, and (b) the ODE approximations for the two algorithms coincide, and little is known about the stability of this ODE.
no code implementations • 5 Oct 2019 • Amirhossein Taghvaei, Prashant G. Mehta
For this algorithm, the equations for empirical mean and covariance are derived and shown to be identical to the Kalman filter.
no code implementations • 10 Jan 2019 • Amirhossein Taghvaei, Prashant G. Mehta
al. 2016) from vector valued variables to probability distributions.
no code implementations • 27 Sep 2018 • Amirhossein Taghvaei, Prashant G. Mehta
In particular, we extend the recent variational formulation of accelerated gradient methods in wibisono2016 from vector valued variables to probability distributions.
no code implementations • NeurIPS 2017 • Amirhossein Taghvaei, Jin W. Kim, Prashant G. Mehta
The formulation is used to provide a complete characterization of the critical points in terms of the solutions of a nonlinear matrix-valued equation, referred to as the characteristic equation.