State Constraint

State constraint research focuses on designing control systems that guarantee safety by preventing trajectories from entering unsafe regions of the state space. Current efforts concentrate on developing algorithms and models, such as control barrier functions (CBFs) and constrained policy optimization methods (e.g., ASCPO), that effectively handle various types of constraints, including hard and soft constraints, and address challenges like high dimensionality and uncertainty. This work is crucial for deploying reinforcement learning and other advanced control techniques in safety-critical applications, such as robotics and autonomous driving, where ensuring system safety is paramount. The development of robust and efficient methods for incorporating state constraints is a significant area of ongoing research with broad implications across multiple fields.