  This work stages Foosball as a versatile platform for advancing scientific
research, particularly in the realm of robot learning. We present an automated
Foosball table along with its corresponding simulated counterpart, showcasing a
diverse range of challenges through example tasks within the Foosball
environment. Initial findings are shared using a simple baseline approach.
Foosball constitutes a versatile learning environment with the potential to
yield cutting-edge research in various fields of artificial intelligence and
machine learning, notably robust learning, while also extending its
applicability to industrial robotics and automation setups. To transform our
physical Foosball table into a research-friendly system, we augmented it with a
2 degrees of freedom kinematic chain to control the goalkeeper rod as an
initial setup with the intention to be extended to the full game as soon as
possible. Our experiments reveal that a realistic simulation is essential for
mastering complex robotic tasks, yet translating these accomplishments to the
real system remains challenging, often accompanied by a performance decline.
This emphasizes the critical importance of research in this direction. In this
concern, we spotlight the automated Foosball table as an invaluable tool,
possessing numerous desirable attributes, to serve as a demanding learning
environment for advancing robotics and automation research.
