[3Q4-GS-8] Robot and real worlds:

[3Q4-GS-8-05] Probabilistic Safety Guarantees for Control Using Neural CBF and Conformal Prediction

〇Koki Hirano¹, Naoya Takeishi¹, Takehisa Yairi¹ (1. Univ. of Tokyo)

Keywords:CBF, Conformal Prediction, Safety Filter

We propose a novel method to ensure safety in controlling nonlinear discrete-time systems. This approach utilizes Control Barrier Functions (CBFs) learned through Neural Networks (NNs) to determine whether the system can maintain a safe state. Additionally, by leveraging Conformal Prediction (CP), we assess the error between the predicted and true values of the CBFs, providing probabilistic safety guarantees.We applied this method to a robot performing path planning using the potential field method and demonstrated its applicability to hazardous states, such as deadlocks, which are challenging to formalize. Furthermore, CP enabled us to quantify the probability of the system maintaining a safe state.By combining data-driven control design with probabilistic safety assurances, this method contributes to enhancing the safety of control systems.