1 citations · 1 across the 4 of their papers we have counts for
5 papers
Control Foundation Models across Changing Block Diagrams: Generalizing Time-Series Prediction to Dynamic Graphs
Yuji Okamoto, Kenta Hoshino, Masaki Inoue
In the prediction and control of physical systems, combining records from different experiments and operations is expected to supply information that no single experiment provides…
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation
Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno +5
We study the reinforcement learning (RL) problem in a constrained Markov decision process (CMDP), where an agent explores the environment to maximize the expected cumulative reward…
Nonlinear receding-horizon differential game for drone racing along a three-dimensional path
Kijin Sung, Kenta Hoshino, Akihiko Honda +2
Drone racing requires high-speed navigation through three-dimensional paths, posing significant challenges in control engineering. Existing control methods lack a feedback control…
Near-Optimal Policy Identification in Robust Constrained Markov Decision Processes via Epigraph Form
Toshinori Kitamura, Tadashi Kozuno, Wataru Kumagai +6
Designing a safe policy for uncertain environments is crucial in real-world control systems. However, this challenge remains inadequately addressed within the Markov decision proce…
Physics-Informed Representation and Learning: Control and Risk Quantification
Zhuoyuan Wang, Reece Keller, Xiyu Deng +3
Optimal and safety-critical control are fundamental problems for stochastic systems, and are widely considered in real-world scenarios such as robotic manipulation and autonomous d…