  Despite recent breakthroughs, the ability of deep learning and reinforcement
learning to outperform traditional approaches to control physically embodied
robotic agents remains largely unproven. To help bridge this gap, we created
the 'AI Driving Olympics' (AI-DO), a competition with the objective of
evaluating the state of the art in machine learning and artificial intelligence
for mobile robotics. Based on the simple and well specified autonomous driving
and navigation environment called 'Duckietown', AI-DO includes a series of
tasks of increasing complexity -- from simple lane-following to fleet
management. For each task, we provide tools for competitors to use in the form
of simulators, logs, code templates, baseline implementations and low-cost
access to robotic hardware. We evaluate submissions in simulation online, on
standardized hardware environments, and finally at the competition event. The
first AI-DO, AI-DO 1, occurred at the Neural Information Processing Systems
(NeurIPS) conference in December 2018. The results of AI-DO 1 highlight the
need for better benchmarks, which are lacking in robotics, as well as improved
mechanisms to bridge the gap between simulation and reality.
