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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A Hybrid Framework for Understanding and Predicting Human Reaching Motions

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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An Inverse Optimal Control Approach to Explain Human Arm Reaching Control Based on Multiple Internal Models

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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Guided Sequential Manipulation Planning Using a Hierarchical Policy

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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A General Framework to Increase Safety of Learning Algorithms for Dynamical Systems Based on Region of Attraction Estimation

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

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

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

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

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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Self-Refined Large Language Model as Automated Reward Function Designer for Deep Reinforcement Learning in Robotics

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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Enabling Versatility and Dexterity of the Dual-Arm Manipulators: A General Framework Toward Universal Cooperative Manipulation

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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Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward

Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang, Da Song, Lei Ma
ArXiv Preprint:2404.08517, April, 2024
In this work, we conduct a comprehensive evaluation of the effectiveness of existing online safety analysis methods on LLMs.
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Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation

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

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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GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning Algorithms Based on Reduced Order Markov Decision Process Model

Zhehua Zhou, Xuan Xie, Jiayang Song, Zhan Shu, Lei Ma
ArXiv Preprint:2406.03912, June, 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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Multilingual Blending: LLM Safety Alignment Evaluation with Language Mixture

Jiayang Song, Yuheng Huang, Zhehua Zhou, Lei Ma
ArXiv Preprint:2407.07342, 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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