arXiv subjects

Alwin Hoffmann

Publications and source records attributed to Alwin Hoffmann.

6 recordsLinked to original sources

arXiv2026

SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation

The fact that robots are getting deployed more often in dynamic environments, together with the increasing complexity of their software systems, raises the need for self-adaptive approaches. In these environments robotic software systems increasingly encounter (1) failures whose symptoms are easy to observe but root causes might be ambiguous or (2) multiple failures appearing concurrently. We present SUNSET, a ROS2-based exemplar that enables rigorous, repeatable evaluation of architecture-based self-adaptation in such conditions. It implements a sensor fusion semantic-segmentation pipeline driven by a trained Machine Learning (ML) model whose input preprocessing can be perturbed to induce realistic performance degradations. The exemplar exposes five observable failures, each of which can be caused by different faults and supports concurrent failures spanning self-healing and self-optimisation. SUNSET includes the segmentation pipeline, a trained ML model, fault-injection scripts, a baseline controller for further comparisons, and step-by-step integration and evaluation documentation to facilitate reproducible studies. The code is available at https://github.com/XITASO/sunset.

cs.RO↗

arXiv2025

Who Is Responsible? Self-Adaptation Under Multiple Concurrent Failures With Unknown Faults in Complex Robotic Systems

Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a single failure cascading across components. Therefore, multiple strategies can be plausible to resolve the underlying fault. Most existing selfadaptive approaches that have been applied to robotics assume predefined one-to-one failure-to-adaptation mappings. We present a ROS2-based self-adaptation approach building upon MAPE-K that addresses (1) multiple simultaneous failures with differing criticality, (2) cascading failures across components, and (3) multiple plausible resolving strategies per detected failure. Central to our approach is an adaptation rule set which lets designers specify failure patterns, assign criticality levels, and enumerate multiple plausible adaptation strategies. This rule set, combined with an automatically extracted live dependency graph, enables lightweight root-cause analysis and strategy ranking to prioritize minimal and effective adaptations. Our approach implements a lightweight self-optimizing component which learns estimated success probabilities of different strategies for each known failure. Experiments on an underwater robot scenario and a perception use case show that our approach can identify root causes among concurrent failures, favors inexpensive adaptations, reduces unnecessary adaptations, and achieves performance comparable to existing baselines designed for sequential failures. The code is publicly available.

cs.RO↗

arXiv2025

Masked Autoencoder Self Pre-Training for Defect Detection in Microelectronics

While transformers have surpassed convolutional neural networks (CNNs) in various computer vision tasks, microelectronics defect detection still largely relies on CNNs. We hypothesize that this gap is due to the fact that a) transformers have an increased need for data and b) (labelled) image generation procedures for microelectronics are costly, and data is therefore sparse. Whereas in other domains, pre-training on large natural image datasets can mitigate this problem, in microelectronics transfer learning is hindered due to the dissimilarity of domain data and natural images. We address this challenge through self pre-training, where models are pre-trained directly on the target dataset, rather than another dataset. We propose a resource-efficient vision transformer (ViT) pre-training framework for defect detection in microelectronics based on masked autoencoders (MAE). We perform pre-training and defect detection using a dataset of less than 10,000 scanning acoustic microscopy (SAM) images. Our experimental results show that our approach leads to substantial performance gains compared to a) supervised ViT, b) ViT pre-trained on natural image datasets, and c) state-of-the-art CNN-based defect detection models used in microelectronics. Additionally, interpretability analysis reveals that our self pre-trained models attend to defect-relevant features such as cracks in the solder material, while baseline models often attend to spurious patterns. This shows that our approach yields defect-specific feature representations, resulting in more interpretable and generalizable transformer models for this data-sparse domain.

cs.CV↗

arXiv2020

Opportunities and Limitations of Mixed Reality Holograms in Industrial Robotics

This paper introduces two case studies combining the field of industrial robotics with Mixed Reality (MR). The goal of those case studies is to get a better understanding of how MR can be useful and what are the limitations. The first case study describes an approach to visualize the digital twin of a robot arm. The second case study aims at facilitating the commissioning of industrial robots. Furthermore, this paper reports the experiences gained by implementing those two scenarios and discusses the limitations.

cs.RO↗

arXiv2013

A Graphical Language for Real-Time Critical Robot Commands

Industrial robotics is characterized by sophisticated mechanical components and highly-developed real-time control algorithms. However, the efficient use of robotic systems is very much limited by existing proprietary programming methods. In the research project SoftRobot, a software architecture was developed that enables the programming of complex real-time critical robot tasks with an object-oriented general purpose language. On top of this architecture, a graphical language was developed to ease the specification of complex robot commands, which can then be used as part of robot application workflows. This paper gives an overview about the design and implementation of this graphical language and illustrates its usefulness with some examples.

cs.RO↗

arXiv2010

On reverse-engineering the KUKA Robot Language

Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of extensibility. For that reason, we developed the extensible Robotics API for programming industrial robots on top of a general-purpose language. Although being a very flexible approach to programming industrial robots, a fully-fledged language can be too complex for simple tasks. Additionally, legacy support for code written in the original DSL has to be maintained. For these reasons, we present a lightweight implementation of a typical robotic DSL, the KUKA Robot Language (KRL), on top of our Robotics API. This work deals with the challenges in reverse-engineering the language and mapping its specifics to the Robotics API. We introduce two different approaches of interpreting and executing KRL programs: tree-based and bytecode-based interpretation.

cs.RO↗