Structural Priors for Active Learning on Robots
A primary hindrance to neural networks in robotic applications is data efficiency; collecting data on a real robot is slow and expensive. Active learning, in which the learner chooses the data that will best accelerate learning, has been shown to reduce data requirements in machine learning and stat...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143133 https://orcid.org/ 0000-0002-5345-3041 |