Robust Koopman Control Barrier Filters for Safe Actor-Critic Reinforcement Learning
Safe reinforcement learning (RL) for robotic systems requires policies that improve task performance while satisfying state and input constraints during both training and deployment. Control barrier functions (CBFs) provide a principled mechanism for enforcing forward invariance through minimally invasive safety filters, but their use in model-free RL is limited by the need for accurate dynamics and hand-designed barrier certificates. We propose Robust Koopman-CBF SAC, a safety-filtered actor--critic framework that learns a finite-dimensional Koopman predictor from data, constructs affine CBF constraints in the lifted space, and enforces them through a quadratic-program safety layer. To a...
| Authors | Dhruv S. Kushwaha, Zoleikha A. Biron |
|---|---|
| Primary category | cs.RO |
| Published | 26 May 2026 |
| Last updated at source | 5 June 2026 |
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