Conservative Constraint

Conservative constraint research focuses on ensuring the performance of autonomous systems, particularly in reinforcement learning and robotics, remains above a predefined safety threshold throughout operation. Current work explores methods like sequential linear programming for trajectory optimization in robotics and importance sampling or mixture policies in reinforcement learning to guarantee this constraint while maintaining efficiency. These techniques address challenges in safety-critical applications, such as autonomous driving and robotic manipulation, by providing provable performance guarantees and mitigating risks associated with unexpected situations or insufficient training data. The ultimate goal is to enable the safe and reliable deployment of intelligent systems in real-world environments.