4 citations · 4 across the 14 of their papers we have counts for
15 papers
Control Realization of Schrödinger Bridges over Discrete-Time Nonlinear Systems via Maximum-Entropy Distribution Steering
Kaito Ito, Kenji Kashima
This paper shows how a Schrodinger bridge (SB) structure emerges from maximum-entropy (MaxEnt) distribution steering of discrete-time nonlinear systems. We also establish that the…
Constrained minmax density transportation for linear parabolic PDEs: a numerical optimal control perspective
Siddhartha Ganguly, Vaibhav Upadhyay, Kenji Kashima +1
This article introduces a numerical optimal control framework for minmax constrained density control for a class of noisy linear parabolic partial differential equations (PDEs), in…
Mutual Information Optimal Density Control of Linear Systems and Generalized Schrödinger Bridges with Reference Refinement
Shoju Enami, Kenji Kashima
We consider a mutual information (MI) regularized optimal density control of a discrete-time linear system. MI optimal control has been utilized for exploration in reinforcement le…
Unbalanced Optimal Transport and Density Control for Discrete-Time Linear Systems
Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima
This article studies unbalanced optimal transport (UOT) and its dynamical extension, unbalanced density control (UDC), for a class of constrained discrete-time linear systems. UOT…
Globally Solving Unbalanced Optimal Transport and Density Control for Gaussian Distributions
Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima
In this article, we study unbalanced optimal transport (UOT) and establish a control-theoretic dynamical extension, which we call the unbalanced density control (UDC), for a class…
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
This paper proposes a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and ope…
Robust maximum hands-off optimal control: existence, maximum principle, and - equivalence
Siddhartha Ganguly, Kenji Kashima
This work advances the maximum hands-off sparse control framework by developing a robust counterpart for constrained linear systems with parametric uncertainties. The resulting opt…
Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
Data-driven control increasingly relies on deep models for complex systems whose first-principles models are difficult to obtain. For reliable deployment, however, learned dynamics…
Data-Driven Density Steering via the Gromov-Wasserstein Optimal Transport Distance
Haruto Nakashima, Siddhartha Ganguly, Kenji Kashima
We tackle the data-driven chance-constrained density steering problem using the Gromov-Wasserstein metric. The underlying dynamical system is an unknown linear controlled recursion…
Formation Shape Control using the Gromov-Wasserstein Metric
Haruto Nakashima, Siddhartha Ganguly, Kohei Morimoto +1
This article introduces a formation shape control algorithm, in the optimal control framework, for steering an initial population of agents to a desired configuration via employing…
Risk-sensitive control as inference with Rényi divergence
Kaito Ito, Kenji Kashima
This paper introduces the risk-sensitive control as inference (RCaI) that extends CaI by using Rényi divergence variational inference. RCaI is shown to be equivalent to log-probabi…
Minimum energy density steering of linear systems with Gromov-Wasserstein terminal cost
Kohei Morimoto, Kenji Kashima
In this paper, we newly formulate and solve the optimal density control problem with Gromov-Wasserstein (GW) terminal cost in discrete-time linear Gaussian systems. Differently fro…
Sampled-Data Primal-Dual Gradient Dynamics in Model Predictive Control
Ryuta Moriyasu, Sho Kawaguchi, Kenji Kashima
Model Predictive Control (MPC) is a versatile approach capable of accommodating diverse control requirements that holds significant promise for a broad spectrum of industrial appli…
Learning Exactly Linearizable Deep Dynamics Models
Ryuta Moriyasu, Masayuki Kusunoki, Kenji Kashima
Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the…
Maximum Entropy Density Control of Discrete-Time Linear Systems with Quadratic Cost
Kaito Ito, Kenji Kashima
This paper addresses the problem of steering the distribution of the state of a discrete-time linear system to a given target distribution while minimizing an entropy-regularized c…