| 書(shū)目名稱 | Robot Learning from Human Teachers |
| 編輯 | Sonia Chernova,Andrea L. Thomaz |
| 視頻video | http://file.papertrans.cn/832/831053/831053.mp4 |
| 叢書(shū)名稱 | Synthesis Lectures on Artificial Intelligence and Machine Learning |
| 圖書(shū)封面 |  |
| 描述 | Learning from Demonstration (LfD) explores techniques for learning a task policy from examples provided by a human teacher. The field of LfD has grown into an extensive body of literature over the past 30 years, with a wide variety of approaches for encoding human demonstrations and modeling skills and tasks. Additionally, we have recently seen a focus on gathering data from non-expert human teachers (i.e., domain experts but not robotics experts). In this book, we provide an introduction to the field with a focus on the unique technical challenges associated with designing robots that learn from naive human teachers. We begin, in the introduction, with a unification of the various terminology seen in the literature as well as an outline of the design choices one has in designing an LfD system. Chapter 2 gives a brief survey of the psychology literature that provides insights from human social learning that are relevant to designing robotic social learners. Chapter 3 walks through an LfD interaction, surveying the design choices one makes and state of the art approaches in prior work. First, is the choice of input, how the human teacher interacts with the robot to provide demonstra |
| 出版日期 | Book 2014 |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-01570-0 |
| isbn_softcover | 978-3-031-00442-1 |
| isbn_ebook | 978-3-031-01570-0Series ISSN 1939-4608 Series E-ISSN 1939-4616 |
| issn_series | 1939-4608 |
| copyright | Springer Nature Switzerland AG 2014 |
|Archiver|手機(jī)版|小黑屋|
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