@inproceedings{HoecherlPohltNiedersteineretal., author = {H{\"o}cherl, Johannes and Pohlt, Clemens and Niedersteiner, Sascha and Schlegl, Thomas and Haug, Sonja and Weber, Karsten}, title = {Entwicklung eines industriellen Assistenzsystems unter Einbeziehung der Anlagenbediener}, series = {2. OTH Clusterkonferenz}, booktitle = {2. OTH Clusterkonferenz}, organization = {OTH Regensburg und OTH Amberg-Weiden}, pages = {11 -- 15}, language = {de} } @inproceedings{HaugGlashauserGrossmannetal., author = {Haug, Sonja and Glashauser, Lisa and Großmann, Benjamin and Pohlt, Clemens and Schlegl, Thomas and Wackerbarth, Alena and Weber, Karsten}, title = {Gamification im Anlernprozess am Industriearbeitsplatz}, series = {2. OTH Clusterkonferenz}, booktitle = {2. OTH Clusterkonferenz}, organization = {OTH Regensburg und OTH Amberg-Weiden}, pages = {183 -- 188}, language = {de} } @inproceedings{WeberHaugSchlegletal., author = {Weber, Karsten and Haug, Sonja and Schlegl, Thomas and Pohlt, Clemens and H{\"o}cherl, Johannes}, title = {Kollaborative Robotik als Beispiel der Mensch-Maschine-Interaktion}, series = {Tagung Technikfolgenabsch{\"a}tzung, 19.06.2017, Akademie der Wissenschaften Wien}, booktitle = {Tagung Technikfolgenabsch{\"a}tzung, 19.06.2017, Akademie der Wissenschaften Wien}, language = {de} } @inproceedings{HoecherlNiedersteinerHaugetal., author = {H{\"o}cherl, Johannes and Niedersteiner, Sascha and Haug, Sonja and Pohlt, Clemens and Schlegl, Thomas and Weber, Karsten and Berlehner, Thomas}, title = {Smart Workbench}, series = {2. OTH Clustertagung, 18.01.2017, Regensburg}, booktitle = {2. OTH Clustertagung, 18.01.2017, Regensburg}, language = {de} } @inproceedings{HaugGlashauserGrossmannetal., author = {Haug, Sonja and Glashauser, Lisa and Großmann, Benjamin and Pohlt, Clemens and Schlegl, Thomas and Wackerbarth, Alena and Weber, Karsten}, title = {Gamification im Anlernprozess am Industriearbeitsplatz - ein inklusiver Ansatz}, series = {2. Transdisziplin{\"a}ren Konferenz "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen", 12.-13.12.2016, Helmut- Schmidt-Universit{\"a}t Hamburg}, booktitle = {2. Transdisziplin{\"a}ren Konferenz "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen", 12.-13.12.2016, Helmut- Schmidt-Universit{\"a}t Hamburg}, editor = {Weidner, Robert}, pages = {421 -- 430}, abstract = {Um die Lebensqualit{\"a}t und Einsatzf{\"a}higkeit von Menschen mit Behinderung oder {\"a}lteren Menschen zu verbessern, wurde untersucht, inwiefern Gamification-Anwendungen beim Anlernen einer Gestensteuerung geeignet sind. Grundlage des Experiments stellt ein intelligenter Arbeitsplatz (Smart Workbench, SWoB) dar, der Personen bei manuellen Handhabungsaufgaben unterst{\"u}tzt sowie bestimmte Produktionsprozesse teilautomatisiert ausf{\"u}hrt. Um die Anlage bedienen zu k{\"o}nnen, muss im Vorfeld eine Einweisung erfolgen, welche von Menschen oder durch ein Lerntutorial mit Gamification-Elementen zur Motivationssteigerung durchgef{\"u}hrt werden kann. In der Studie wurde untersucht, welche Form des Anleitens aus welchen Gr{\"u}nden von unterschiedlichen Personen eher akzeptiert oder abgelehnt wird.}, language = {de} } @inproceedings{HoecherlNiedersteinerHaugetal., author = {H{\"o}cherl, Johannes and Niedersteiner, Sascha and Haug, Sonja and Pohlt, Clemens and Schlegl, Thomas and Weber, Karsten and Berlehner, Thomas}, title = {Smart Workbench}, series = {2. Transdisziplin{\"a}ren Konferenz "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen", 12.-13.12.2016, Helmut-Schmidt-Universit{\"a}t, Hamburg}, booktitle = {2. Transdisziplin{\"a}ren Konferenz "Technische Unterst{\"u}tzungssysteme, die die Menschen wirklich wollen", 12.-13.12.2016, Helmut-Schmidt-Universit{\"a}t, Hamburg}, editor = {Weidner, Robert}, pages = {49 -- 50}, language = {de} } @inproceedings{PohltSchleglWachsmuth, author = {Pohlt, Clemens and Schlegl, Thomas and Wachsmuth, Sven}, title = {Human Work Activity Recognition for Working Cells in Industrial Production Contexts}, series = {2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6-9 Oct. 2019, Bari, Italy}, booktitle = {2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 6-9 Oct. 2019, Bari, Italy}, doi = {10.1109/SMC.2019.8913873}, pages = {4225 -- 4230}, abstract = {Collaboration between robots and humans requires communicative skills on both sides. The robot has to understand the conscious and unconscious activities of human workers. Many state-of-the-art activity recognition algorithms with high performance rates on existing benchmark datasets are available for this task. This paper re-evaluates appropriate architectures in light of human work activity recognition for working cells in industrial production contexts. The specific constraints of such a domain is elaborated and used as prior knowledge. We utilize state-of-the-art algorithms as spatiotemporal feature encoders and search for appropriate classification and fusion strategies. Furthermore, we combine keypoint-based with appearance-based approaches to a multi-stream recognition system. Due to data protection rules and the high effort of data annotation within industrial domains only small datasets are available that reflect production aspects. Therefore, we use transfer learning approaches to reduce the dependency on data volume and variance in the target domain. The resulting recognition system achieves high performance for both singular person action and human-object interaction.}, language = {en} } @inproceedings{PohltHaubnerLangetal., author = {Pohlt, Clemens and Haubner, Franz and Lang, Jonas and Rochholz, Sandra and Schlegl, Thomas and Wachsmuth, Sven}, title = {Effects on User Experience During Human-Robot Collaboration in Industrial Scenarios}, series = {2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7-10 Oct. 2018, Miyazaki, Japan}, booktitle = {2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 7-10 Oct. 2018, Miyazaki, Japan}, publisher = {IEEE}, doi = {10.1109/SMC.2018.00150}, pages = {837 -- 842}, abstract = {In smart manufacturing environments robots collaborate with human operators as peers. They even share the same working space and time. An intuitive interaction with different input modalities is decisive to reduce workload and training periods for collaboration. We introduce our interaction system that is able to recognize gestures, actions and objects in a typical smart working scenario. As key aspect, this article considers an empirical investigation of input modalities (touch, gesture), individual differences (performance, recognition rate, previous knowledge) and boundary conditions (level of automation) on user experience. Therefore, answers from 31 participants within two experiments are collected. We show that the arrangement of the human-robot collaboration (input modalities, boundary conditions) has a significant effect on user experience in real-world environments. This effect and the individual differences between participants can be measured utilizing recognition rates and standardized usability questionnaires.}, language = {en} } @inproceedings{PohltHellSchlegletal., author = {Pohlt, Clemens and Hell, Sebastian and Schlegl, Thomas and Wachsmuth, Sven}, title = {Impact of Spontaneous Human Inputs during Gesture based Interaction on a Real-World Manufacturing Scenario}, series = {Proceedings of the 5th International Conference on Human Agent Interaction (HAI '17), Bielefeld Germany, 17.10.2017 -20.10.2017}, booktitle = {Proceedings of the 5th International Conference on Human Agent Interaction (HAI '17), Bielefeld Germany, 17.10.2017 -20.10.2017}, editor = {Wrede, Britta and Nagai, Yukie and Komatsu, Takanori and Hanheide, Marc and Natale, Lorenzo}, publisher = {ACM}, address = {New York, NY}, isbn = {9781450351133}, doi = {10.1145/3125739.3132590}, pages = {347 -- 351}, abstract = {Seamless human-robot collaboration depends on high non-verbal behaviour recognition rates. To realize that in real-world manufacturing scenarios with an ecological valid setup, a lot of effort has to be invested. In this paper, we evaluate the impact of spontaneous inputs on the robustness of human-robot collaboration during gesture-based interaction. A high share of these spontaneous inputs lead to a reduced capability to predict behaviour and subsequently to a loss of robustness. We observe body and hand behaviour during interactive manufacturing of a collaborative task within two experiments. First, we analyse the occurrence frequency, reason and manner of human inputs in specific situations during a human-human experiment. We show the high impact of spontaneous inputs, especially in situations that differ from the typical working procedure. Second, we concentrate on implicit inputs during a real-world Wizard of Oz experiment using our human-robot working cell. We show that hand positions can be used to anticipate user needs in a semi-structured environment by applying knowledge about the semi-structured human behaviour which is distributed over working space and time in a typical manner.}, language = {en} } @inproceedings{PohltSchleglWachsmuth, author = {Pohlt, Clemens and Schlegl, Thomas and Wachsmuth, Sven}, title = {Weakly-Supervised Learning for Multimodal Human Activity Recognition in Human-Robot Collaboration Scenarios}, series = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): October 25-29, 2020, Las Vegas, NV, USA (virtual)}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS): October 25-29, 2020, Las Vegas, NV, USA (virtual)}, publisher = {IEEE}, doi = {10.1109/IROS45743.2020.9340788}, pages = {8381 -- 8386}, abstract = {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.}, language = {en} } @article{NiedersteinerLangPohltetal., author = {Niedersteiner, Sascha and Lang, Jonas and Pohlt, Clemens and Schlegl, Thomas}, title = {Klassifikation des Arbeitsfortschritts}, series = {atp magazin}, volume = {59}, journal = {atp magazin}, number = {11}, publisher = {Vulkan-Verlag}, doi = {10.17560/atp.v59i11.1911}, pages = {58 -- 66}, abstract = {Steigende Anforderungen an die Qualit{\"a}t von zum Teil manuell gefertigten Produkten f{\"u}hren dazu, dass Handarbeitspl{\"a}tze mit Assistenzsystemen f{\"u}r die Unterst{\"u}tzung der am Arbeitsplatz arbeitenden Mitarbeiterinnen und Mitarbeiter ausgestattet werden. Der Beitrag beschreibt einen neuen Ansatz, um mittels Verfahren des maschinellen Lernens die Objekterkennung sowie die Transitionen eines, den Arbeitsprozess repr{\"a}sentierenden Zustandsautomaten eines solchen Systems einzulernen. Hierf{\"u}r werden nach einer Vorverarbeitung Daten aus einer Tiefenkamera in drei Stufen durch Support Vector Machines (SVM) klassifiziert und das Ergebnis mit dem Zustandsautomaten verkn{\"u}pft. Das Konzept wird an einem industriellen Montageprozess {\"u}berschaubarer Komplexit{\"a}t evaluiert; es zeigt gute Ergebnisse hinsichtlich der Robustheit gegen{\"u}ber Fehlern bei der Objektklassifikation.}, language = {de} } @article{NiedersteinerLangPohltetal., author = {Niedersteiner, Sascha and Lang, Jonas and Pohlt, Clemens and Schlegl, Thomas}, title = {Tracking work task completion - A support vector machine approach for assisting workbenches}, series = {atp edition}, journal = {atp edition}, number = {11}, publisher = {DIV Deutscher Industrieverl.}, doi = {10.17560/atp.v59i11.1911}, pages = {58 -- 66}, abstract = {Tightening quality requirements of industrial products involving manual assembly lead to the development of assisting workbenches with integrated functions to support workers performing these manual tasks. This contribution discusses a new approach to learning transitions of a finite state automaton representing the sequence of work tasks based on the video stream of a 3D depth camera. Preprocessed video data is fed into a three-stage classification scheme based on support vector machines. The results of the classification are then related to the state automation to trigger state transitions indicating the completion of a specific work task and the start of the next one. The proposed approach has been evaluated at an industrial assembly process of moderate complexity and shows very robust results with respect to disturbances caused by inaccurate object classification.}, language = {en} } @inproceedings{NiedersteinerPohltSchlegl, author = {Niedersteiner, Sascha and Pohlt, Clemens and Schlegl, Thomas}, title = {Smart Workbench: A Multimodal and Bidirectional Assistance System for Industrial Application}, series = {IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 09.-12.11.2015, Yokohama, Japan}, booktitle = {IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 09.-12.11.2015, Yokohama, Japan}, publisher = {IEEE}, address = {Piscataway, N.J.}, isbn = {978-1-4799-1762-4}, doi = {10.1109/iecon.2015.7392549}, abstract = {Manual tasks in industrial production are often monotonous, leading to a decrease in concentration and motivation of the worker and thus to deficiencies in the products. With quality as well as performance requirements getting more and more stringent, workers need additional support by their work environment. We developed a novel approach providing worker assistance and inline quality assurance for manual workplaces. The prototypical system Smart Workbench (SWoB) uses a multimodal sensor interface consisting of a 3D depth sensor in combination with a 2D camera to control quality aspects of the product and track the work progress. The bidirectional flow of information is handled via an image processing driven gestural interface and the displaying of advice directly on the work surface. In this paper the developed system is introduced and the current state of evaluating its industrial usage with a manual quality control and packaging task reported.}, language = {en} } @misc{ScharfenbergMottokArtmannetal., author = {Scharfenberg, Georg and Mottok, J{\"u}rgen and Artmann, Christina and Hobelsberger, Martin and Paric, Ivan and Großmann, Benjamin and Pohlt, Clemens and Wackerbarth, Alena and Pausch, Uli and Heidrich, Christiane and Fadanelli, Martin and Elsner, Michael and P{\"o}cher, Daniel and Pittroff, Lenz and Beer, Stefan and Br{\"u}ckl, Oliver and Haslbeck, Matthias and Sterner, Michael and Thema, Martin and Muggenthaler, Nicole and Lenck, Thorsten and G{\"o}tz, Philipp and Eckert, Fabian and Deubzer, Michael and Stingl, Armin and Simsek, Erol and Kr{\"a}mer, Stefan and Großmann, Benjamin and Schlegl, Thomas and Niedersteiner, Sascha and Berlehner, Thomas and Joblin, Mitchell and Mauerer, Wolfgang and Apel, Sven and Siegmund, Janet and Riehle, Dirk and Weber, Joachim and Palm, Christoph and Zobel, Martin and Al-Falouji, Ghassan and Prestel, Dietmar and Scharfenberg, Georg and Mandl, Roland and Deinzer, Arnulf and Halang, W. and Margraf-Stiksrud, Jutta and Sick, Bernhard and Deinzer, Renate and Scherzinger, Stefanie and Klettke, Meike and St{\"o}rl, Uta and Wiech, Katharina and Kubata, Christoph and Sindersberger, Dirk and Monkman, Gareth J. and Dollinger, Markus and Dembianny, Sven and K{\"o}lbl, Andreas and Welker, Franz and Meier, Matthias and Thumann, Philipp and Swidergal, Krzysztof and Wagner, Marcus and Haug, Sonja and Vernim, Matthias and Seidenst{\"u}cker, Barbara and Weber, Karsten and Arsan, Christian and Schone, Reinhold and M{\"u}nder, Johannes and Schroll-Decker, Irmgard and Dillinger, Andrea Elisabeth and Fuchshofer, Rudolf and Monkman, Gareth J. and Shamonin (Chamonine), Mikhail and Geith, Markus A. and Koch, Fabian and {\"U}hlin, Christian and Schratzenstaller, Thomas and Saßmannshausen, Sean Patrick and Auchter, Eberhard and Kriz, Willy and Springer, Othmar and Thumann, Maria and Kusterle, Wolfgang and Obermeier, Andreas and Udalzow, Anton and Schmailzl, Anton and Hierl, Stefan and Langer, Christoph and Schreiner, Rupert}, title = {Forschungsbericht 2015}, editor = {Baier, Wolfgang}, address = {Regensburg}, organization = {Ostbayerische Technische Hochschule Regensburg}, isbn = {978-3-00-048589-3}, doi = {10.35096/othr/pub-1386}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-13867}, language = {de} }