5 papers
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
This paper presents 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…
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…