Zobrazeno 1 - 10
of 465
pro vyhledávání: '"Mehta, Prashant"'
Autor:
Wang, Tixian, Chang, Heng-Sheng, Kim, Seung Hyun, Guo, Jiamiao, Akcal, Ugur, Walt, Benjamin, Biskup, Darren, Halder, Udit, Krishnan, Girish, Chowdhary, Girish, Gazzola, Mattia, Mehta, Prashant G.
A neural network-based framework is developed and experimentally demonstrated for the problem of estimating the shape of a soft continuum arm (SCA) from noisy measurements of the pose at a finite number of locations along the length of the arm. The n
Externí odkaz:
http://arxiv.org/abs/2409.12443
This paper is concerned with the design of algorithms based on systems of interacting particles to represent, approximate, and learn the optimal control law for reinforcement learning (RL). The primary contribution of the present paper is to show tha
Externí odkaz:
http://arxiv.org/abs/2406.11057
Autor:
Kim, Jin Won, Mehta, Prashant G.
Duality between estimation and control is a foundational concept in Control Theory. Most students learn about the elementary duality -- between observability and controllability -- in their first graduate course in linear systems theory. Therefore, i
Externí odkaz:
http://arxiv.org/abs/2405.07650
In this paper, a backward map is introduced for the purposes of analysis of the nonlinear (stochastic) filter stability. The backward map is important because the filter-stability in the sense of $\chisq$-divergence follows from showing a certain var
Externí odkaz:
http://arxiv.org/abs/2405.01127
This paper is divided into two parts. The first part reviews the formulae for f-divergences in the study of continuous-time Markov processes and explores their applications in areas such as stochastic stability, the second law of thermodynamics, and
Externí odkaz:
http://arxiv.org/abs/2404.15779
In this paper, stochastic optimal control problems in continuous time and space are considered. In recent years, such problems have received renewed attention from the lens of reinforcement learning (RL) which is also one of our motivation. The main
Externí odkaz:
http://arxiv.org/abs/2404.06696
Autor:
Wang, Tixian, Halder, Udit, Gribkova, Ekaterina, Gillette, Rhanor, Gazzola, Mattia, Mehta, Prashant G.
In this article, a biophysically realistic model of a soft octopus arm with internal musculature is presented. The modeling is motivated by experimental observations of sensorimotor control where an arm localizes and reaches a target. Major contribut
Externí odkaz:
http://arxiv.org/abs/2402.01074
Publikováno v:
Astrophys Space Sci 368, 97 (2023)
In this paper, we study the evolutions of a self-gravitating cloud of bosonic dark matter with finite angular momentum and self-interaction. This is achieved by using the sixth-order pseudospectral operator splitting method to solve the system of non
Externí odkaz:
http://arxiv.org/abs/2311.12789
In this report, passive elasticity properties of $\textit{Octopus rubescens}$ arm tissue are investigated using a multidisciplinary approach encompassing biomechanical experiments, computational modeling, and analyses. Tensile tests are conducted to
Externí odkaz:
http://arxiv.org/abs/2311.01798
Autor:
Kim, Jin Won, Mehta, Prashant G.
Publikováno v:
IEEE Transactions on Automatic Control, 2024
This paper is concerned with the problem of nonlinear (stochastic) filter stability of a hidden Markov model (HMM) with white noise observations. A contribution is the variance decay property which is used to conclude filter stability. For this purpo
Externí odkaz:
http://arxiv.org/abs/2305.12850