Rafael Paintner | Searcharxiv

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↗