While machine learning methods demonstrated impressive success in many application domains, their impact on real robotic platforms is still far from their potential.To unleash the capabilities of machine learning in the field of robotics, researchers need to cope with specific challenges and issues of the real world. While many robotics benchmarks are available for machine learning, most simplify the complexity of classical robotics tasks, for example neglecting highly nonlinear dynamics of the actuators, such as stiction. We organize the robot air hockey challenge, which allows machine learning researchers to face the sim-to-real-gap in a complex and dynamic environment while competing with each other. In particular, the challenge focuses on robust, reliable, and safe learning techniques suitable for real-world robotics. Through this challenge, we wish to investigate how machine learning techniques can outperform standard robotics approaches in challenging robotic scenarios while dealing with safety, limited data usage, and real-time requirements.