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To deliver the benefits of autonomous driving safely, quickly, and broadly, learnability has to be a key element of the solution. In this talk, I will describe Aurora's philosophy towards building learnability into the self-driving architecture, avoiding the pitfalls of applying vanilla ML to problems involving feedback, and leveraging expert demonstrations for learning decision-making models. I will conclude with our approach to testing and validation.
Author Information
Venkatraman Narayanan (Aurora Innovation)
James Bagnell (Aurora Innovation)
Drew has worked for two decades at the intersection of machine learning and robotics both as a faculty member at Carnegie Mellon University and in engagements with industry from self-driving haul trucks to perception architecture for Uber’s self-driving cars and in his current role as CTO of Aurora Innovation.
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