Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang, Da Song, Lei Ma
IEEE Transactions on Artificial Intelligence, August, 2025
In this work, we conduct a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs.
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Zhehua Zhou, Xuan Xie, Jiayang Song, Zhan Shu, Lei Ma
IEEE Transactions on Neural Networks and Learning Systems, November, 2024
In this work, we introduce a Genralizable Safety enhancer (GenSafe) to improve the safety performance of safe reinforcement learning algorithms.
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Yi Ren*, Zhehua Zhou* (equal contribution), Ziwei Xu, Yang Yang, Guangyao Zhai, Marion Leibold, Fenglei Ni, Zhengyou Zhang, Martin Buss, Yu Zheng
IEEE Transactions on Robotics, February, 2024
In this article, we propose a general and versatile control framework for dual-arm manipulators.
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Cong Li, Qingchen Liu, Zhehua Zhou, Martin Buss, Fangzhou Liu
IEEE Transactions on Systems, Man, and Cybernetics: Systems, October, 2022
This article proposes an off-policy risk-sensitive reinforcement learning (RL)-based control framework to jointly optimize the task performance and constraint satisfaction in a disturbed environment.
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Zhehua Zhou, Ozgur S Oguz, Marion Leibold, Martin Buss
IEEE Transactions on Neural Networks and Learning Systems, September, 2021
In this article, we propose a general data-driven model order reduction approach for safe reinforcement learning.
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Zhehua Zhou, Ozgur S Oguz, Marion Leibold, Martin Buss
IEEE Transactions on Robotics, June, 2020
In this article, we propose a computationally effective and general safe learning framework, specifically for complex dynamical systems.
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Ozgur S Oguz, Zhehua Zhou, Stefan Glasauer, Dirk Wollherr
Scientific Reports, April, 2018
In this study, we utilize an Inverse Optimal Control (IOC) framework to find the combination of internal models for different human reaching motions.
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Ozgur S Oguz, Zhehua Zhou, Dirk Wollherr
Frontiers in Robotics and AI, March, 2018
In this work, we propose a hybrid framework for understanding and predicting human reaching motions for human-robot collaboration.
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Zhijie Wang, Zhehua Zhou, Jiayang Song, Yuheng Huang, Zhan Shu, Lei Ma
2025 ACM International Conference on the Foundations of Software Engineering (FSE), June, 2025
We present VLATest, a fuzzing framework designed to generate robotic manipulation scenes for testing VLA models.
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Jiayang Song, Yuheng Huang, Zhehua Zhou, Lei Ma
2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), July, 2024
In this work, we introduce Multilingual Blending, a mixed-language query-response scheme designed to evaluate the safety alignment of various state-of-the-art LLMs.
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Zhehua Zhou, Jiayang Song, Kunpeng Yao, Zhan Shu, Lei Ma
2024 IEEE International Conference on Robotics and Automation (ICRA), May, 2024
In this work, we introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process.
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Zhehua Zhou, Jiayang Song, Xuan Xie, Zhan Shu, Lei Ma, Dikai Liu, Jianxiong Yin, Simon See
IEEE/ACM 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP), April, 2024
As a foundational step towards building reliable AI-enabled robotics systems, in this paper, we propose a public benchmark for robotics manipulation.
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Hoai My Van, Ozgur S Oguz, Zhehua Zhou, Marc Toussaint
Robotics: Science and Systems (RSS) Conference 2020 - Learning in Task and Motion Planning Workshop, June, 2020
In this work, we introduce a hierarchical policy structure that selects high-level actions for effective task and motion planning (TAMP) in sequential manipulation tasks.
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Ozgur S Oguz, Volker Gabler, Gerold Huber, Zhehua Zhou, Dirk Wollherr
2016 International Symposium on Experimental Robotics (ISER), March, 2017
In this work, we present a Human-Robot-Collaboration (HRC) framework consisting of a hybrid human motion prediction approach together with a game theoretical action selection.
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Renzhi Wang, Zhehua Zhou, Jiayang Song, Xuan Xie, Xiaofei Xie, Lei Ma
ArXiv Preprint:2408.03892, August, 2024
We propose MORTAR, a runtime action repair framework designed for AI-CPSs in this work
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Jiayang Song*, Zhehua Zhou* (equal contribution), Jiawei Liu, Chunrong Fang, Zhan Shu, Lei Ma
ArXiv Preprint:2309.06687, September, 2023
In this work, we propose a novel LLM framework with a self-refinement mechanism for automated reward function design.
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Xuan Xie, Jiayang Song, Zhehua Zhou, Fuyuan Zhang, Lei Ma
ArXiv Preprint:2305.03882, May, 2023
In this work, we propose Mosaic, a model-based safety analysis framework for AI-enabled cyber-physical systems (AI-CPSs).
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Zhehua Zhou, Ozgur S Oguz, Yi Ren, Marion Leibold, Martin Buss
ArXiv Preprint:2109.05077, September, 2021
In this work, we investigate how different training data will affect the safe reinforcement learning approach based on data-driven model order reduction.
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