6 citations · 6 across the 4 of their papers we have counts for
4 papers
Avoiding Semi-Infinite Programming in Distributionally Robust Control Based on Mean-Variance Metrics
Yuma Shida, Yuji Ito
Conventional stochastic control methods have several limitations. They focus on optimizing the average performance and, in some cases, performance variability; however, their probl…
Explicit Reformulation of Discrete Distributionally Robust Optimization Problems
Yuma Shida, Yuji Ito
Distributionally robust optimization (DRO) is an effective framework for controlling real-world systems with various uncertainties, typically modeled using distributional uncertain…
Discrete Distributionally Robust Optimal Control with Explicitly Constrained Optimization
Yuma Shida, Yuji Ito
Distributionally robust optimal control (DROC) is gaining interest. This study presents a reformulation method for discrete DROC (DDROC) problems to design optimal control policies…
Reinforcement Learning of Multi-robot Task Allocation for Multi-object Transportation with Infeasible Tasks
Yuma Shida, Tomohiko Jimbo, Tadashi Odashima +1
Multi-object transport using multi-robot systems has the potential for diverse practical applications such as delivery services owing to its efficient individual and scalable coope…