<div class="csl-bib-body">
<div class="csl-entry">Hirschmanner, M., & Vincze, M. (2022). Robot Learning from Humans in Everyday Life Scenarios. In S. T. Köszegi & M. Vincze (Eds.), <i>Trust in Robots</i> (pp. 179–199). TU Wien Academic Press. https://doi.org/10.34727/2022/isbn.978-3-85448-052-5_8</div>
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Robots need to be able to learn about novel environments and acquire new capabilities during deployment. Robot
learning from humans is a paradigm to enable the human user to teach robots certain information and skills without
programming knowledge. In this chapter, we provide an overview of this domain and present some of our work as
concrete examples. First, we address grounded language learning with the goal to create connections between
words and references (e.g., objects, locations) in social environments. We present our incremental word learning
systems using the Pepper robot. Following that, we introduce to learning low-level actions from demonstrations.
We present our systems with an industrial robotic arm and a dexterous robotic hand. Then, we address the role of
the teacher in the learning process. We investigate the human factors that are important for facilitating the learning
process and present the results of our user studies. We conclude with open challenges and opportunities for further
research.
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dc.language.iso
en
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dc.rights.uri
http://creativecommons.org/licenses/by-sa/4.0/
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dc.subject
robot learning
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dc.subject
human-robot interaction
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dc.subject
learning from demonstration
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dc.subject
grounded language learning
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dc.title
Robot Learning from Humans in Everyday Life Scenarios
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dc.type
Book Contribution
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dc.type
Buchbeitrag
de
dc.rights.license
Creative Commons Attribution-ShareAlike 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International