3 papers
eess.SY2026
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…
cs.RO2024
Visual-Based Forklift Learning System Enabling Zero-Shot Sim2Real Without Real-World Data
Koshi Oishi, Teruki Kato, Hiroya Makino +1
Forklifts are used extensively in various industrial settings and are in high demand for automation. In particular, counterbalance forklifts are highly versatile and employed in di…
math.OC2024
Continuation Method for Nonsmooth Model Predictive Control Using Proximal Technique
Ryotaro Shima, Ryuta Moriyasu, Teruki Kato
This paper presents a novel framework for the continuation method of model predictive control based on optimal control problem with a nonsmooth regularizer. Via the proximal operat…