Learning to Play Foosball: System and Baselines

TL;DR

This paper introduces an automated Foosball platform and simulation environment to facilitate research in robot learning, demonstrating initial baseline results and highlighting challenges in transferring skills from simulation to real robots.

Contribution

The work presents a novel automated Foosball system with a simulated counterpart, establishing a new versatile platform for advancing AI and robotics research.

Findings

Simulation is crucial for mastering complex robotic tasks.

Transferring skills from simulation to real robots remains challenging.

Foosball offers a demanding environment for AI and robotics research.

Abstract

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.