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.
Author: | Clemens Pohlt, Thomas SchleglORCiD, Sven Wachsmuth |
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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 |