Keywords: Transfer Learning, Reward Design, Commonsense
TL;DR: The primary challenge in building generalizable robotic systems may not be in the learning algorithms or the hardware, but in how humans transfer their knowledge to robots.
Abstract: Robots are commonly used for several industrial applications and some have made their mark even in households (e.g., the roomba). Undoubtedly these systems are impressive! However, they are very narrow in their functionality and we are not even close to building a robot butler. A central challenge is the ability to work with sensory observations and generalization to novel situations. While we do not prescribe a solution to this problem, we do provide a perspective on a few dominant ideas in robot learning for multi-task learning and generalization. This perspective suggests a counter-intuitive conclusion: the primary challenge in building generalizable robotic systems (e.g., a robot butler) is not in the learning algorithms or the hardware, but in how humans transfer their knowledge to robots.
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