3 papers
math.OC2026
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…
math.OC2025
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…
cs.RO2025
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…