Hard Constraint

Hard constraints, in the context of machine learning and control systems, refer to strict limitations that must be satisfied by a system's behavior, unlike soft constraints which can be violated with a penalty. Current research focuses on developing efficient algorithms and model architectures, such as those based on imitation learning, constrained Markov decision processes, and differentiable optimization, to reliably enforce these hard constraints within various applications, including robotics, optimization problems, and physics-informed neural networks. This work is crucial for deploying machine learning models in safety-critical applications where violations of constraints are unacceptable, improving the reliability and trustworthiness of AI systems in real-world scenarios.