Conference
Aug 2026
Meta-Learning Control Barrier Functions under Input Constraints
This paper studies learning-based synthesis of control barrier functions (CBFs) with an explicit treatment of hard input constraints. Training a task-specific CBF from scratch for each new environment can be computationally expensive and data-inefficient. To address this, we propose a meta-learning approach based on Mo...
Wataru Hashimoto, Masato Sakamoto, Kazumune Hashimoto et al.
· Conference on Control Techno... · 0 citations