4 citations · 13 across the 30 of their papers we have counts for
16 papers · 1 filter
Physics-informed Reinforcement Learning for Stochastic Reach-Avoid Analysis
Hikaru Hoshino, Yorie Nakahira
Stochastic reach-avoid analysis of controlled dynamical systems is an important tool for safety-critical control under uncertainty, in which the reach-avoid probability is characte…
Fractional Risk Analysis of Stochastic Systems with Jumps and Memory
Yimeng Sun, Zhuoyuan Wang, Xiaole Zhang +4
Accurate risk assessment is essential for safety-critical autonomous and control systems under uncertainty. In many real-world settings, stochastic dynamics exhibit asymmetric jump…
Online Adaptive Probabilistic Safety Certificate with Language Guidance
Zhuoyuan Wang, Xiyu Deng, Hikaru Hoshino +1
Achieving long-term safety in uncertain/extreme environments while accounting for human preferences remains a fundamental challenge for autonomous systems. Existing methods often t…
Safe Driving in Occluded Environments
Zhuoyuan Wang, Tongyao Jia, Pharuj Rajborirug +5
Ensuring safe autonomous driving in the presence of occlusions poses a significant challenge in its policy design. While existing model-driven control techniques based on set invar…
Multi-Level Multi-Fidelity Methods for Path Integral and Safe Control
Zhuoyuan Wang, Takashi Tanaka, Yongxin Chen +1
Sampling-based approaches are widely used in systems without analytic models to estimate risk or find optimal control. However, gathering sufficient data in such scenarios can be p…
Neural Spline Operators for Risk Quantification in Stochastic Systems
Zhuoyuan Wang, Raffaele Romagnoli, Kamyar Azizzadenesheli +1
Accurately quantifying long-term risk probabilities in diverse stochastic systems is essential for safety-critical control. However, existing sampling-based and partial differentia…