Robot reinforcement learning: safety in real-world applications

How can we make a robot learn in the real world while ensuring safety? In this work, we show how it's possible to face this problem. The key idea to exploit domain knowledge and use the constraint definition to our advantage. Following our approach, it's possible to implement learning robotic agents that can explore and learn in an arbitrary environment while ensuring safety at the same time. Safety is a fundamental feature in real-world robotics applications: robots should not cause damage to the environment, to themselves, and they must ensure the safety of people operating around them.