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Item type:Publication, Access status: without files , Simulation-only training for real-world localization integrity monitoring(IEEE, 2026-09); ; ; ; Reliable self-localization is a prerequisite for the safe operation of autonomous mobile robots. Particle-filter-based methods such as adaptive Monte Carlo localization (AMCL) may silently fail under dynamic disturbances, map ambiguities, or motion uncertainty. This paper presents a data-driven localization monitoring framework that detects localization failures using Light Detection and Ranging (LiDAR) scans, occupancy maps, and particle filter statistics. The monitoring model is trained exclusively in simulation using randomized environments, without real-world training data. Gradient-boosting models are employed to capture nonlinear consistency relations, and temporal feature aggregation is introduced to model failure dynamics over time. Experiments in simulation and on real robotic platforms demonstrate reliable failure detection and generalization to unseen environments and motion uncertainty levels. Cross-platform transfer to a different robot is evaluated and identified as a limitation. Simulation-only training with domain randomization achieves an F1 score of 0.86 on real-world data.Publicationtype: Conference Paper8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Learnability of models for cyber-physical systems — a reviewThe complexity of Cyber-Physical Systems (CPS) emerges from a tight integration of numerous digital and physical components. Recent powerful machine learning algorithms are increasingly applied to learn models of CPS from data. However, theoretical limitations of such a pragmatic approach are often left aside. In this paper, we review the learnability of such data-driven models for CPS. Learnability is a key aspect in all parts of the model learning process which seeks for guarantees and preconditions for formal properties of a learned model such as accuracy, robustness, and interpretability. We review the state-of-the-art in model learning for CPS that focuses on these learnability aspects. We arrange and discuss this state-of-the-art alongside the steps of the model learning process, being (1) data quality assurance, (2) model learning, (3) model evaluation, and (4) model refinement. Additionally, we analyze the involvement of prior knowledge and its influence on model learnability. Finally, we discuss the implications of our findings for the development of models for CPS and identify directions for future research, e.g., incorporating prior knowledge more deeply into the model learning process.Publicationtype: Book Part14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-driven probabilistic evaluation of logic properties with PAC-confidence on Mealy machinesCyber-Physical Systems (CPS) are complex systems that require powerful models for tasks like verification, diagnosis, or debugging. Often, suitable models are not available and manual extraction is difficult. Data-driven approaches then provide a solution to, e.g., diagnosis tasks and verification problems based on data collected from the system. In this paper, we consider CPS with a discrete abstraction in the form of a Mealy machine. We propose a data-driven approach to determine the safety probability of the system on a finite horizon of n time steps. The approach is based on the Probably Approximately Correct (PAC) learning paradigm. Thus, we elaborate a connection between discrete logic and probabilistic reachability analysis of systems, especially providing an additional confidence on the determined probability. The learning process follows an active learning paradigm, where new learning data is sampled in a guided way after an initial learning set is collected. We validate the approach with a case study on an automated lane-keeping system.Publicationtype: Conference Paper7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Dynamics-based identification of hybrid systems using symbolic regression(IEEE, 2024-08); ; ; ; Travé-Massuyès, LouiseSymbolic regression has shown potential in the identification of physical systems. Hybrid systems, which combine both continuous and discrete behavior, are a relevant extension of purely physical systems, used in many fields, including robotics, biological systems, and control systems. However, due to their complexity, finding an accurate model is a challenge. This paper presents a novel approach to learning models of hybrid systems using symbolic regression. Our method leverages the power of genetic programming to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. Symbolic regression detects transitions between different continuous behavior of a system directly based on the dynamics, instead of pure distances of observed trajectories. Furthermore, models generated by symbolic regression can be used to predict future system behavior, detect anomalies, and identify the underlying dynamics of the system while providing a human-readable representation. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real system represented in a hybrid model, providing a valuable tool for system identification and diagnosis.Publicationtype: Conference Paper21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards robustness evaluation of models for cyber-physical systems extended abstractThis paper presents a novel approach to evaluate robustness of models for Cyber-Physical Systems (CPS). We consider a CPS to be a hybrid system and learn models from data using decision tree regressors. We define robustness as the capability of the learned model to maintain its performance under varying unseen conditions. By systematically incorporating a perturbation process and calculating key statistical measures on the differences in model predictions, our method provides a comprehensive evaluation framework when modeling CPS. This approach enhances our understanding of a model’s robustness in the face of uncertainties, ultimately contributing to the development of more robust models for CPS. We demonstrate the application of our method for a simulated temperature control system.Publicationtype: Conference Paper56 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards the automatic generation of models for prediction, monitoring, and testing of cyber-physical systems(IEEE, 2023-09); ; ; 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-Driven Test Generation for Black-Box Systems from Learned Decision Tree Models(2023-05); Testing of black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables model-based testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. In this scope, we define a test coverage metric for decision tree models. Furthermore, we identify different modes of testing and explain that a decision tree model especially facilitates model-based testing for black-box systems with limited controllability of inputs and the inability to reset the system to a specific state. A case study on a discrete system illustrates our MBT approach.Publicationtype: Conference Paper10 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Extended Abstract: Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree Models(2023-03); Testing black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables Model-Based Testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. A decision tree model especially facilitates MBT for black-box systems if no system reset is possible. A case study on a discrete system illustrates our MBT approach.Publicationtype: Conference Paper9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Data-Driven Test Generation for Black-Box Systems From Learned Decision Tree ModelsTesting black-box systems is a difficult task, because no prior knowledge on the system is given that can be used for design and evaluation of tests. Learning a model of a black-box system from observations enables Model-Based Testing (MBT). We take a recent approach using decision tree learning to create a model of a black-box system and discuss the usage of such a decision tree model for test generation. A decision tree model especially facilitates MBT for black-box systems if no system reset is possible. A case study on a discrete system illustrates our MBT approach.Publicationtype: Conference Paper20 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Regression Trees for System Models and PredictionToday's systems are highly complex and often appear as black-box systems because of unknown internal functionalities. Thus, retrieving a model of a system is possible only based on observations of the system. We explore the usage of regression trees to learn a model of a complex system with continuous signals. Our approach for learning a regression tree uses observed inputs and outputs of a system from bounded history. We describe how to construct such a model and analyze the accuracy of predictions. Results show the applicability of regression tree models for continuous systems.Publicationtype: Conference Paper22