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Weakly-Supervised Learning for Multimodal Human Activity Recognition in Human-Robot Collaboration Scenarios

  • The ability to synchronize expectations among human-robot teams and understand discrepancies between expectations and reality is essential for human-robot collaboration scenarios. To ensure this, human activities and intentions must be interpreted quickly and reliably by the robot using various modalities. In this paper we propose a multimodal recognition system designed to detect physical interactions as well as nonverbal gestures. Existing approaches feature high post-transfer recognition rates which, however, can only be achieved based on well-prepared and large datasets. Unfortunately, the acquisition and preparation of domain-specific samples especially in industrial context is time consuming and expensive. To reduce this effort we introduce a weakly-supervised classification approach. Therefore, we learn a latent representation of the human activities with a variational autoencoder network. Additional modalities and unlabeled samples are incorporated by a scalable product-of-expert sampling approach. The applicability in industrial context is evaluated by two domain-specific collaborative robot datasets. Our results demonstrate, that we can keep the number of labeled samples constant while increasing the network performance by providing additional unprocessed information.

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Metadaten
Author:Clemens Pohlt, Thomas SchleglORCiD, Sven Wachsmuth
DOI:https://doi.org/10.1109/IROS45743.2020.9340788
Parent Title (English):2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): October 25-29, 2020, Las Vegas, NV, USA (virtual)
Publisher:IEEE
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2020
Release Date:2022/02/09
Tag:Activity recognition; Collaboration; Intelligent robots; Service robots; Synchronization; reliability
First Page:8381
Last Page:8386
Institutes:Fakultät Elektro- und Informationstechnik
Fakultät Maschinenbau
Begutachtungsstatus:peer-reviewed
research focus:Produktion und Systeme
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG