activity
20202023
most citedTowards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

41 citations · 47 across the 4 of their papers we have counts for

collaborators

6 papers

cs.RO2023★ 3 cited

ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning

Zhehua Zhou, Jiayang Song, Kunpeng Yao +2

Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into th…

cs.SE2023★ 41 cited

Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

Zhehua Zhou, Jiayang Song, Xuan Xie +5

As a representative cyber-physical system (CPS), robotic manipulator has been widely adopted in various academic research and industrial processes, indicating its potential to act…

cs.SE2023★ 3 cited

Mosaic: Model-based Safety Analysis Framework for AI-enabled Cyber-Physical Systems

Xuan Xie, Jiayang Song, Zhehua Zhou +2

Cyber-physical systems (CPSs) are now widely deployed in many industrial domains, e.g., manufacturing systems and autonomous vehicles. To further enhance the capability and applica…

eess.SY2021

Data Generation Method for Learning a Low-dimensional Safe Region in Safe Reinforcement Learning

Zhehua Zhou, Ozgur S. Oguz, Yi Ren +2

Safe reinforcement learning aims to learn a control policy while ensuring that neither the system nor the environment gets damaged during the learning process. For implementing saf…

cs.RO2020

Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems

Zhehua Zhou, Ozgur S. Oguz, Marion Leibold +1

For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning…

eess.SY2020

Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control

Cong Li, Qingchen Liu, Zhehua Zhou +2

This paper proposes an off-policy risk-sensitive reinforcement learning based control framework for stabilization of a continuous-time nonlinear system that subjects to additive di…