In recent years, robotic locomotion has made considerable advances, allowing legged robots to walk successfully across a wide range of terrains. Many of the most successful approaches utilize Reinforcement Learning (RL) as a framework that allows robots to learn useful behaviors to achieve a goal through a trial-and-error approach, specifying only the desired objective. However, while RL has demonstrated impressive capabilities, it also presents some limitations. One of the main concerns is the lack of safety during both the learning process and the implementation of the resulting policy. In real-world scenarios, unsafe behavior can damage the robot, harm the surrounding area, or even become a risk to humans.