<div class="csl-bib-body">
<div class="csl-entry">Willibald, C., & Lee, D. (2022). Multi-Level Task Learning Based on Intention and Constraint Inference for Autonomous Robotic Manipulation. In <i>IROS 2022 Kyōto - IEEE/RSJ International Conference on Intelligent Robots and Systems</i> (pp. 7688–7695). https://doi.org/10.1109/IROS47612.2022.9981288</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193422
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dc.description.abstract
To perform tasks in unstructured environments, robots need to be able to apply learned skills to different contexts and to autonomously make decisions online. We, therefore, developed a novel data-driven task learning approach that segments a task demonstration into simpler skills and structures them in a high-level task graph. In contrast to other state-of-the-art methods, the presented approach can not only infer the low-level skills and their respective subgoals but also multimodal feature constraints fitted individually to each skill. The inferred feature constraints allow to detect anomalies during autonomous task execution, which can be automatically resolved by a recovery behavior of the task graph. The subgoals encode each skill's intention and thereby enable to flexibly transition between skills and to generalize the behavior to new setups. By separating the subgoal and constraint inference, we achieve a reduced computational complexity and an increased performance compared to state-of-the-art task learning approaches. In a real-world manipulation task, we demonstrate the reusability of skills as well as the autonomous decision-making of our approach.
en
dc.language.iso
en
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dc.subject
Learning from Demonstration
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dc.subject
task graph
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dc.subject
anomaly detection
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dc.title
Multi-Level Task Learning Based on Intention and Constraint Inference for Autonomous Robotic Manipulation
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
9781665479271
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dc.description.startpage
7688
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dc.description.endpage
7695
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
IROS 2022 Kyōto - IEEE/RSJ International Conference on Intelligent Robots and Systems
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tuw.peerreviewed
true
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tuw.researchTopic.id
I3
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tuw.researchTopic.name
Automation and Robotics
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tuw.researchTopic.value
100
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tuw.publication.orgunit
E384-03 - Forschungsbereich Autonomous Systems
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tuw.publisher.doi
10.1109/IROS47612.2022.9981288
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dc.description.numberOfPages
8
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tuw.author.orcid
0000-0003-3579-4130
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tuw.author.orcid
0000-0003-1897-7664
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tuw.event.name
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)