3 papers
cs.CV2026
Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
Nikolai Röhrich, Julian GleiÃner, Ahmed H. A. Ibrahim +2
Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While syn…
cs.RO2026
Who Is Responsible? Self-Adaptation Under Multiple Concurrent Failures With Unknown Faults in Complex Robotic Systems
Andreas Wiedholz, Rafael Paintner, Julian GleiÃner +2
Robotic systems increasingly operate in dynamic, unpredictable environments, where tightly coupled sensors and software modules increase the probability of a single failure cascadi…
cs.RO2026
SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation
Andreas Wiedholz, Rafael Paintner, Julian GleiÃner +3
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 a…