TY - CONF A1 - Pohlt, Clemens A1 - Schlegl, Thomas A1 - Wachsmuth, Sven T1 - Weakly-Supervised Learning for Multimodal Human Activity Recognition in Human-Robot Collaboration Scenarios T2 - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): October 25-29, 2020, Las Vegas, NV, USA (virtual) N2 - 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. KW - Activity recognition KW - Collaboration KW - Intelligent robots KW - reliability KW - Service robots KW - Synchronization Y1 - 2020 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/2704 SP - 8381 EP - 8386 PB - IEEE ER -