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cs.RO2026
Deep QP Safety Filter: Model-free Learning for Reachability-based Safety Filter
Byeongjun Kim, H. Jin Kim
We introduce Deep QP Safety Filter, a fully data-driven safety layer for black-box dynamical systems. Our method learns a Quadratic-Program (QP) safety filter without model knowled…
cs.RO2025
EigenSafe: A Spectral Framework for Learning-Based Probabilistic Safety Assessment
Inkyu Jang, Jonghae Park, Sihyun Cho +3
We present EigenSafe, an operator-theoretic framework for safety assessment of learning-enabled stochastic systems. In many robotic applications, the dynamics are inherently stocha…
cs.RO2025
Enhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter
Dabin Kim, Inkyu Jang, Youngsoo Han +2
Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailabl…