5 papers
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…
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…
Invariance Guarantees using Continuously Parametrized Control Barrier Functions
Inkyu Jang, H. Jin Kim
Constructing a control invariant set with an appropriate shape that fits within a given state constraint is a fundamental problem in safety-critical control but is known to be diff…
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…
Estimation of Constraint Admissible Invariant Set with Neural Lyapunov Function
Dabin Kim, H. Jin Kim
Constraint admissible positively invariant (CAPI) sets play a pivotal role in ensuring safety in control and planning applications, such as the recursive feasibility guarantee of e…