High-dimensional stochastic optimal control using continuous tensor decompositions
Motion planning and control problems are embedded and essential in almost all robotics applications. These problems are often formulated as stochastic optimal control problems and solved using dynamic programming algorithms. Unfortunately, most existing algorithms that guarantee convergence to optim...
Main Authors: | Gorodetsky, Alex Arkady, Karaman, Sertac, Marzouk, Youssef M |
---|---|
Other Authors: | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
Format: | Article |
Published: |
SAGE Publications
2019
|
Online Access: | http://hdl.handle.net/1721.1/120322 https://orcid.org/0000-0003-3152-8206 https://orcid.org/0000-0002-2225-7275 https://orcid.org/0000-0001-8242-3290 |
Similar Items
-
A continuous analogue of the tensor-train decomposition
by: Gorodetsky, Alex, et al.
Published: (2020) -
Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation
by: Gorodetsky, Alex Arkady
Published: (2017) -
Low-rank Tensor Integration for Gaussian Filtering of Continuous Time Nonlinear Systems
by: Gorodetsky, Alex Arkady, et al.
Published: (2021) -
Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems
by: Gorodetsky, Alex A., et al.
Published: (2021) -
Continuous Tensor Train-Based Dynamic Programming for High-Dimensional Zero-Sum Differential Games
by: Tal, Ezra, et al.
Published: (2021)