The field of robotics is moving towards developing more sophisticated safety-aware control methods. Researchers are focusing on creating systems that can proactively identify and avoid potential hazards, including those that may not have been explicitly programmed or anticipated. This involves developing new techniques for modeling and predicting uncertainty, as well as designing control systems that can adapt to changing circumstances and prioritize safety. Notable papers in this area include:

• Uncertainty-aware Latent Safety Filters for Avoiding Out-of-Distribution Failures, which introduces a new approach to detecting potential hazards by calibrating an uncertainty threshold via conformal prediction.
• Learning Neural Control Barrier Functions from Offline Data with Conservatism, which proposes an algorithm for training control barrier functions from offline datasets to prevent systems from reaching unsafe states.
• Skill-based Safe Reinforcement Learning with Risk Planning, which exploits auxiliary offline demonstration data to enhance effective safe reinforcement learning.
• Bridging Model Predictive Control and Deep Learning for Scalable Reachability Analysis, which leverages model predictive control techniques to guide and accelerate the reachability learning process.