The paper reviews the factors limiting the accuracy of locating a fiber optic cable fault when using an optical time domain reflectometer (OTDR) and describes an error estimation method for typical use cases. The primary source of errors lies in the complex relationship between the length of the optical fiber (measured by OTDR), its routing, cable design depending on cable design and type of installation (i.e. duct, directly buried, aerial) as well as the spare lengths used for service purposes. The techniques which considerably improve the accuracy of the fault localization processes are presented, the importance of accurate documentation of the network and of referencing the fault location to the nearest splice instead of end of the line are discussed, as is the absence of cable helix factor in data sheets.

Multi-purpose service robots must execute their tasks reliably in different situations, as well as learn from humans and explain their plans to them. We address these issues by introducing a knowledge representation scheme to facilitate skill generalization and explainability. This scheme allows representing knowledge of the robot’s understanding of a scene and performed task. We also present techniques for extracting this knowledge from raw data. Such knowledge representation and extraction methods have not been explored adequately in previous research. Our approach does not require any prior knowledge or 3D models of the objects involved. Moreover, the representation scheme is easy to understand for humans. The system is modular so that new recognition or reasoning routines can be added without changing the basic architecture. We developed a computer vision system and a task reasoning module that works with our knowledge representation. The efficacy of our approach is demonstrated with two different tasks: hanging items on pegs and stacking one item on another. A formalization of our knowledge representation scheme is presented, showing how the system is capable of learning from a few demonstrations.

Gestures play an integral role in human communication. Our research aims to develop a gesture understanding system that allows for better interpretation of human instructions in household robotics settings. We conducted an experiment with 34 participants who used pointing gestures to teach concepts to an assistant. Gesture data were analyzed using manual annotations (MAXQDA) and the computational methods of pose estimation and k-means clustering. The study revealed that participants tend to maintain consistent pointing styles, with one-handed pointing and index finger gestures being the most common. Gaze and pointing often co-occur, as do leaning forward and pointing. Using our gesture categorization algorithm, we analyzed gesture information values. As the experiment progressed, the information value of gestures remained stable, although the trends varied between participants and were associated with factors such as age and gender. These findings underscore the need for gesture recognition systems to balance generalization with personalization for more effective human–robot interaction.

Continual learning poses a fundamental challenge for neural systems, which typically suffer from catastrophic forgetting when exposed to sequential tasks. Self-Organizing Maps (SOMs), despite their inherent interpretability and efficiency, also exhibit this vulnerability. In this paper, we introduce Saturation Self-Organizing Maps (SatSOM)—an extension designed to enhance knowledge retention in continual learning scenarios. Sat-SOM incorporates a novel saturation mechanism that progressively reduces the learning rate and neighborhood radius of neurons as they accumulate information. This dynamic effectively stabilizes well-trained neurons, redirecting new learning to underutilized regions of the map. To further accommodate tasks of unknown complexity, we introduce a dynamic variant capable of adaptive grid expansion. We evaluate SatSOM on sequential versions of the FashionMNIST and KMNIST datasets, showing that it significantly outperforms existing SOM-based methods and approaches the retention capabilities of a k-nearest neighbors (kNN) baseline. Ablation studies confirm the critical role of the saturation mechanism. SatSOM offers a lightweight and interpretable solution for sequential learning and provides a foundation for implementing adaptive plasticity in complex architectures.