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Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo Vision and Gesture control 6

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Authentication and Verification in Human-Robot Cooperative R...

6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Kovacs, Gabor Sziranyi, Tamas HUN-REN Institute for Computer Science and Control Machine Perception Research Laboratory Budapest Hungary

ISBN: (纸本)9798331506520

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators' movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation. © 2025 IEEE.

关键词： Human robot interaction

Robust control and data reconstruction for nonlinear epidemiological models using feedback linearization and state estimation

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Mathematical Biosciences and Engineering 2025年第1期22卷 109-137页

作者： Csutak, Balázs Szederkényi, Gábor Faculty of Information Technology and Bionics Pázmány Péter Catholic University Práter u. 50/A BudapestH-1083 Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

It has been clearly demonstrated over the past years that control theory can provide an efficient framework for the solution of several complex tasks in epidemiology. In this paper, we present a computational approach for the state estimation based reference tracking control and historical data reconstruction using nonlinear compartmental epidemic models. The control model is given in nonlinear input-affine form, where the manipulable input is the disease transmission rate influenced by possible measures and restrictions, while the observed or computed output is the number of infected people. The control design is built around a simple SEIR model and relies on a feedback linearization technique. We examine and compare different control setups distinguished by the availability of state information, complementing the directly measurable data with an extended Kalman filter used for state estimation. To illustrate the capabilities and robustness of the proposed method, we carry out multiple case studies for output tracking and data reconstruction on Swedish and hungarian data, all in the presence of serious model and parameter mismatch. Computation results show that a well-designed feedback, even in the presence of significant observation uncertainties, can sufficiently reduce the effect of modeling errors. © 2025 the Authors,

关键词： Feedback linearization

Authentication and Verification in Human-Robot Cooperative Robotic Cells using Stereo Vision and Gesture control

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Authentication and Verification in Human-Robot Cooperative R...

International Image Processing, Applications and Systems Conference (IPAS)

作者： Gábor Kovács Tamás Szirányi Machine Perception Research Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

The integration of human-robot interaction (HRI) technologies with industrial automation has become increasingly essential for enhancing productivity and safety in manufacturing environments. In this paper, we propose a novel approach to address these challenges by using stereo vision and gesture control in cooperative robotic cells. Our system enables seamless authentication of operators and real-time verification of task execution, ensuring compliance with established protocols and safety *** features of our system include its gesture-based operation with gesture recognition algorithms, allowing operators to interact with robotic systems intuitively and efficiently. By leveraging stereo vision, our system accurately tracks the operators’ movement within the workspace, facilitating precise task execution and object *** present a detailed description of our system architecture, experimental configuration, and real-world performance assessment. Our results demonstrate the effectiveness and feasibility of our approach in enhancing operational efficiency, ensuring quality, and improving the overall user experience in industrial automation.

关键词： Automation Service robots Tracking Human-robot interaction Authentication Systems architecture User experience Real-time systems Stereo vision Safety

HuAMR: A hungarian AMR Parser and Dataset

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arXiv 2025年

作者： Barta, Botond Hamerlik, Endre Nyist, Milán Konor Ács, Judit HUN-REN Institute for Computer Science and Control Hungary Eötvös Loránd University Hungary

We present HuAMR, the first Abstract Meaning Representation (AMR) dataset and a suite of large language model-based AMR parsers for hungarian, targeting the scarcity of semantic resources for non-English languages. To create HuAMR, we employed Llama-3.1-70B to automatically generate silver-standard AMR annotations, which we then refined manually to ensure quality. Building on this dataset, we investigate how different model architectures — mT5 Large and Llama-3.2-1B — and fine-tuning strategies affect AMR parsing performance. While incorporating silver-standard AMRs from Llama-3.1-70B into the training data of smaller models does not consistently boost overall scores, our results show that these techniques effectively enhance parsing accuracy on hungarian news data (the domain of HuAMR). We evaluate our parsers using Smatch scores and confirm the potential of HuAMR and our parsers for advancing semantic parsing research. © 2025, CC BY.

关键词： Semantics

Automated Linear Parameter-Varying Modeling of Nonlinear Systems: A Global Embedding Approach

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arXiv 2025年

作者： Olucha, E. Javier Koelewijn, Patrick J.W. Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, an automated linear parameter-varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at ***. © 2025, CC BY.

关键词： MATLAB

A cost function approximation method for dynamic vehicle routing with docking and LIFO constraints

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Multimodal Transportation 2025年第1期4卷

作者： Horváth, Markó Kis, Tamás Györgyi, Péter HUN-REN Institute for Computer Science and Control H-1111 Budapest Kende u. 13-17 Hungary

In this paper, we study a dynamic pickup and delivery problem with docking constraints. There is a homogeneous fleet of vehicles to serve pickup-and-delivery requests at given locations. The vehicles can be loaded up to their capacity, while unloading has to follow the last-in-first-out (LIFO) rule. The locations have a limited number of docking ports for loading and unloading, which may force the vehicles to wait. The problem is dynamic since the transportation requests arrive real-time, over the day. Accordingly, the routes of the vehicles are to be determined dynamically. The goal is to satisfy all the requests such that a combination of tardiness penalties and traveling costs is minimized. We propose a cost function approximation based solution method. In each decision epoch, we solve the respective optimization problem with a perturbed objective function to ensure the solutions remain adaptable to accommodate new requests. We penalize waiting times and idle vehicles. We propose a variable neighborhood search based method for solving the optimization problems, and we apply two existing local search operators, and we also introduce a new one. We evaluate our method using a widely adopted benchmark dataset, and the results demonstrate that our approach significantly surpasses the current state-of-the-art methods. © 2025 The Author(s)

关键词： Cost function approximation Docking constraints Dynamic pickup and delivery problem Variable neighborhood search

Do Attention Heads Compete or Cooperate during Counting?

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arXiv 2025年

作者： Zsámboki, Pál Fraknói, Ádám Gedeon, Máté Kornai, András Zombori, Zsolt HUN-REN Alfréd Rényi Institute of Mathematics Budapest Hungary Eötvös Loránd University Budapest Hungary Budapest University of Technology and Economics Budapest Hungary HUN-REN Institute for Computer Science and Control Budapest Hungary

We present an in-depth mechanistic interpretability analysis of training small transformers on an elementary task, counting, which is a crucial deductive step in many algorithms. In particular, we investigate the collaboration/competition among the attention heads: we ask whether the attention heads behave as a pseudo-ensemble, all solving the same subtask, or they perform different subtasks, meaning that they can only solve the original task in conjunction. Our work presents evidence that on the semantics of the counting task, attention heads behave as a pseudo-ensemble, but their outputs need to be aggregated in a nonuniform manner in order to create an encoding that conforms to the syntax. Our source code will be available upon publication. © 2025, CC BY-NC-SA.

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Orthogonal projection-based regularization for efficient model augmentation

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arXiv 2025年

作者： Györök, Bendegúz M. Hoekstra, Jan H. Kon, Johan Péni, Tamás Schoukens, Maarten Tóth, Roland Systems and Control Lab HUN-REN Institute for Computer Science and Control Budapest Hungary Control Systems Group Eindhoven University of Technology Netherlands Control Systems Technology Group Eindhoven University of Technology Netherlands

Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a considerable part of the learning effort is spent on capturing already expected/known behavior due to first-principles-based understanding of some aspects of the system. A potential solution is to integrate prior physical knowledge directly into the model structure, combining the strengths of physics-based modeling and deep-learning-based identification. The most common approach is to use an additive model augmentation structure, where the physics-based and the machine-learning (ML) components are connected in parallel. However, such models are overparametrized, training them is challenging, potentially causing the physics-based part to lose interpretability. To overcome this challenge, this paper proposes an orthogonal projection-based regularization technique to enhance parameter learning, convergence, and even model accuracy in learning-based augmentation of nonlinear baseline models. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

On Space-Filling Input Design for Nonlinear Dynamic Model Learning: A Gaussian Process Approach

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arXiv 2025年

作者： Liu, Yuhan Kiss, Máté Tóth, Roland Schoukens, Maarten Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest1111 Hungary

While optimal input design for linear systems has been well-established, no systematic approach exists for nonlinear systems, where robustness to extrapolation/interpolation errors is prioritized over minimizing estimated parameter variance. To address this issue, we develop a novel identification-oriented space-filling input design strategy for efficient covering of a well-defined region of interest. By placing a Gaussian Process (GP) prior on the joint input-state space, the proposed strategy leverages the GP posterior variance to construct a space-filling promoting input design cost function from the Bayesian point of view. This accordingly enables maximization of the coverage in the region of interest, thereby facilitating the generation of informative datasets. Furthermore, we theoretically prove that minimization of the cost function indicates the space-filling behavior. The effectiveness of the proposed strategy is demonstrated on both an academic example and a mass-spring-damper system, highlighting its potential practical impact on efficient exploration in identification scenarios for nonlinear dynamical systems. © 2025, CC BY-NC-SA.

关键词： Cost functions