Abstract
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 present 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,” the 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. In this paper we will describe the AI-DO 1 including the motivation and design objections, the challenges, the provided infrastructure, an overview of the approaches of the top submissions, and a frank assessment of what worked well as well as what needs improvement. 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.
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Notes
- 1.
AMOD competition website https://www.amodeus.science/.
- 2.
The performance rules of AI-DO http://docs.duckietown.org/DT18/AIDO/out/aido_rules.html.
- 3.
For more information, this technique is described in further depth at the following URL: https://www.balena.io/blog/building-arm-containers-on-any-x86-machine-even-dockerhub/.
- 4.
Duckietown logs database: http://logs.duckietown.org/.
- 5.
Accessible online at https://github.com/iasawseen/MultiServerRL.
- 6.
Any submission is visualized on https://challenges.duckietown.org/v3/ by clicking its submission number.
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Acknowledgements
We would like to thank NeurIPS and in particular Sergio Escalera and Ralf Herbrich for giving us the opportunity to share the AI Driving Olympics with the machine learning community. We are grateful to Amazon AWS and Aptiv for their sponsorship and hands-on help that went into this competition. We are also grateful to the many students in Montréal, Zurich, Taiwan, Boston, Chicago, and many others that have shaped Duckietown and AI-DO into what they are today.
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Zilly, J. et al. (2020). The AI Driving Olympics at NeurIPS 2018. In: Escalera, S., Herbrich, R. (eds) The NeurIPS '18 Competition. The Springer Series on Challenges in Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-29135-8_3
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