Embargoed until 2022-01-01
- Conference Paper
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. © Springer Nature Switzerland AG 2020. Show more
Book titleThe NeurIPS '18 Competition. From Machine Learning to Intelligent Conversations
Journal / seriesThe Springer Series on Challenges in Machine Learning
Pages / Article No.
Organisational unit09574 - Frazzoli, Emilio / Frazzoli, Emilio
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