Learning Task-Parameterized Skills From Few Demonstrations

J. Zhu, Michael Gienger, J. Kober

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)
44 Downloads (Pure)


Moving away from repetitive tasks, robots nowadays demand versatile skills that adapt to different situations. Task-parameterized learning improves the generalization of motion policies by encoding relevant contextual information in the task parameters, hence enabling flexible task executions. However, training such a policy often requires collecting multiple demonstrations in different situations. To comprehensively create different situations is non-trivial thus renders the method less applicable to real-world problems. Therefore, training with fewer demonstrations/situations is desirable. This paper presents a novel concept to augment the original training dataset with synthetic data for policy improvements, thus allows learning task-parameterized skills with few demonstrations.
Original languageEnglish
Pages (from-to)4063-4070
JournalIEEE Robotics and Automation Letters
Issue number2
Publication statusPublished - 2022

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


Dive into the research topics of 'Learning Task-Parameterized Skills From Few Demonstrations'. Together they form a unique fingerprint.

Cite this