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Crowdsourcing for closed-loop control
BibTeX
@MISC{Osentoski_crowdsourcingfor,
author = {Sarah Osentoski and Grayin Jay and Christopher Crick and Odest Chadwicke Jenkins},
title = {Crowdsourcing for closed-loop control},
year = {}
}
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Abstract
We present a system for large scale robotic learning from demonstration. We describe a set of software tools for enabling human-robot interaction over the internet and gathering the large datasets that such crowdsourcing makes possible. We show results in which humans teach a robot to navigate a maze over the Internet. Robots occupy a peculiar place in our culture. We have been building robots in our imaginations for decades, robots that are alternately wondrous or terrifying, always brilliant and consummately skilled. In contrast, real robots are typically brittle and capable of only a few simple constrained tasks. Additionally, only expert programmers, intimately familiar with the particulars of a low-level robotic system, can hope to achieve any kind of complex robot behavior. This paper describes our efforts to apply the lessons of crowdsourcing to robotics to leverage the power and knowledge of a truly large number of end users to create more skilled and robust robot controllers. We focus on learning from demonstration (LfD), [2, 1] an approach to robot programming in which users demonstrate desired skills to a robot. Nothing is required of the user beyond the ability to complete the task in a way that the robot can interpret. Traditionally, LfD research has been constrained
