2025/04/03 by Taekyung Kim, Kee, Robin Inho, Kim, Taekyung +3
Computer Science · Engineering · Physics and Astronomy · #Advanced Control Systems Optimization #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #FOS: Electrical engineering #Model Reduction and Neural Networks #Robotics (cs.RO) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2504.03038
openalex publication_date 2025/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Control barrier functions (CBFs) provide a tractable mechanism for enforcing safety constraints in robotic systems, but their practical performance depends strongly on the choice of class-K function parameters. Under input constraints, conservative parameters often preserve feasibility at the cost of slow progress, whereas aggressive parameters can make the CBF-based optimization infeasible or unsafe. This paper proposes Online Adaptive CBF (OA-CBF), a framework for adapting CBF parameters at runtime. We introduce the notion of locally validated CBF parameters, which certify candidate parameters over a finite prediction horizon, and show that safety is preserved when such validation is maintained over successive update intervals. To identify locally validated parameters efficiently, OA-CBF trains a probabilistic ensemble neural network to evaluate queried CBF parameters rather than directly predict a single parameter. A graph-attention encoder represents variable-size obstacle environments, an epistemic uncertainty gate calibrated by conformal prediction rejects unreliable predictions, and a distributionally robust CVaR condition screens aleatoric risk. Among the verified candidates, OA-CBF selects the parameter with the best predicted progress metric and applies it through either an MPC-CBF or CBF-QP safety filter. Simulation studies on dynamic unicycle, planar and three-dimensional quadrotor, kinematic bicycle, and VTOL quadplane benchmarks show that OA-CBF reduces the conservatism of fixed-parameter CBF controllers while maintaining low collision and infeasibility rates.