5 papers
Confidence-Gated Robot Autonomy: When Does Uncertainty Actually Help?
Johannes A. Gaus, Jhon P. F. Charaja, Daniel Haeufle
Robotic systems often use predictive uncertainty to decide whether to act autonomously or defer to a fallback policy. In threshold-gated autonomy, uncertainty matters mainly throug…
When to Act: Calibrated Confidence for Reliable Human Intention Prediction in Assistive Robotics
Johannes A. Gaus, Winfried Ilg, Daniel Haeufle
Assistive devices must determine both what a user intends to do and how reliable that prediction is before providing support. We introduce a safety-critical triggering framework ba…
SUPER -- A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
Johannes A. Gaus, Daniel Häufle, Woo-Jeong Baek
While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This pape…
GUARD: Toward a Compromise between Traditional Control and Learning for Safe Robot Systems
Johannes A. Gaus, Junheon Yoon, Woo-Jeong Baek +3
This paper presents the framework \textbf{GUARD} (\textbf{G}uided robot control via \textbf{U}ncertainty attribution and prob\textbf{A}bilistic kernel optimization for \textbf{R}is…
Human-Interpretable Uncertainty Explanations for Point Cloud Registration
Johannes A. Gaus, Loris Schneider, Yitian Shi +3
In this paper, we address the point cloud registration problem, where well-known methods like ICP fail under uncertainty arising from sensor noise, pose-estimation errors, and part…