Safe Learning

Safe learning focuses on developing machine learning algorithms that guarantee safety during training and deployment, particularly crucial for safety-critical applications like robotics and autonomous driving. Current research emphasizes methods combining reinforcement learning with safety constraints, often using control barrier functions or model predictive control to ensure that learned policies remain within safe operational bounds, and exploring techniques like safe Bayesian optimization for efficient parameter tuning. This field is vital for enabling the reliable and trustworthy deployment of AI systems in real-world scenarios, bridging the gap between theoretical performance and practical safety requirements.