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Publications (5 of 5) Show all publications
Schreiter, T., Morillo-Mendez, L., Chadalavada, R. T., Rudenko, A., Billing, E., Magnusson, M., . . . Lilienthal, A. J. (2023). Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver. In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN): Proceedings. Paper presented at 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, South Korea, August 28-31, 2023 (pp. 293-300). IEEE
Open this publication in new window or tab >>Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
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2023 (English)In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN): Proceedings, IEEE, 2023, p. 293-300Conference paper, Published paper (Refereed)
Abstract [en]

Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an "Anthropomorphic Robotic Mock Driver" (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE RO-MAN, ISSN 1944-9445, E-ISSN 1944-9437
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-110873 (URN)10.1109/RO-MAN57019.2023.10309629 (DOI)001108678600042 ()9798350336702 (ISBN)9798350336719 (ISBN)
Conference
32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, South Korea, August 28-31, 2023
Funder
EU, Horizon 2020, 101017274 (DARKO)
Available from: 2024-01-22 Created: 2024-01-22 Last updated: 2024-01-22Bibliographically approved
Morillo-Mendez, L., Stower, R., Sleat, A., Schreiter, T., Leite, I., Martinez Mozos, O. & Schrooten, M. G. S. (2023). Can the robot "see" what I see? Robot gaze drives attention depending on mental state attribution. Frontiers in Psychology, 14, Article ID 1215771.
Open this publication in new window or tab >>Can the robot "see" what I see? Robot gaze drives attention depending on mental state attribution
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2023 (English)In: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 14, article id 1215771Article in journal (Refereed) Published
Abstract [en]

Mentalizing, where humans infer the mental states of others, facilitates understanding and interaction in social situations. Humans also tend to adopt mentalizing strategies when interacting with robotic agents. There is an ongoing debate about how inferred mental states affect gaze following, a key component of joint attention. Although the gaze from a robot induces gaze following, the impact of mental state attribution on robotic gaze following remains unclear. To address this question, we asked forty-nine young adults to perform a gaze cueing task during which mental state attribution was manipulated as follows. Participants sat facing a robot that turned its head to the screen at its left or right. Their task was to respond to targets that appeared either at the screen the robot gazed at or at the other screen. At the baseline, the robot was positioned so that participants would perceive it as being able to see the screens. We expected faster response times to targets at the screen the robot gazed at than targets at the non-gazed screen (i.e., gaze cueing effect). In the experimental condition, the robot's line of sight was occluded by a physical barrier such that participants would perceive it as unable to see the screens. Our results revealed gaze cueing effects in both conditions although the effect was reduced in the occluded condition compared to the baseline. These results add to the expanding fields of social cognition and human-robot interaction by suggesting that mentalizing has an impact on robotic gaze following.

Place, publisher, year, edition, pages
Frontiers Media S.A., 2023
Keywords
attention, cueing effect, gaze following, intentional stance, mentalizing, social robots
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-107503 (URN)10.3389/fpsyg.2023.1215771 (DOI)001037081700001 ()37519379 (PubMedID)2-s2.0-85166030431 (Scopus ID)
Funder
EU, European Research Council, 754285Wallenberg AI, Autonomous Systems and Software Program (WASP), RTI2018-095599-A-C22
Note

Funding Agency:

RobWell project - Spanish Ministerio de Ciencia, Innovacion y Universidades

Available from: 2023-08-10 Created: 2023-08-10 Last updated: 2023-09-20Bibliographically approved
Almeida, T., Rudenko, A., Schreiter, T., Zhu, Y., Gutiérrez Maestro, E., Morillo-Mendez, L., . . . Lilienthal, A. (2023). THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction. In: Proceedings of the IEEE/CVF International Conference on Computer Vision: . Paper presented at IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023 (pp. 2200-2209).
Open this publication in new window or tab >>THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction
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2023 (English)In: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, p. 2200-2209Conference paper, Published paper (Refereed)
Abstract [en]

Autonomous systems, that need to operate in human environments and interact with the users, rely on understanding and anticipating human activity and motion. Among the many factors which influence human motion, semantic attributes, such as the roles and ongoing activities of the detected people, provide a powerful cue on their future motion, actions, and intentions. In this work we adapt several popular deep learning models for trajectory prediction with labels corresponding to the roles of the people. To this end we use the novel THOR-Magni dataset, which captures human activity in industrial settings and includes the relevant semantic labels for people who navigate complex environments, interact with objects and robots, work alone and in groups. In qualitative and quantitative experiments we show that the role-conditioned LSTM, Transformer, GAN and VAE methods can effectively incorporate the semantic categories, better capture the underlying input distribution and therefore produce more accurate motion predictions in terms of Top-K ADE/FDE and log-likelihood metrics.

National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-109508 (URN)
Conference
IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP), NT4220EU, Horizon 2020, 101017274 (DARKO)
Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-11-01Bibliographically approved
Schreiter, T., Morillo-Mendez, L., Chadalavada, R. T., Rudenko, A., Billing, E. A. & Lilienthal, A. J. (2022). The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction. In: SCRITA Workshop Proceedings (arXiv:2208.11090): . Paper presented at 31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022.
Open this publication in new window or tab >>The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction
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2022 (English)In: SCRITA Workshop Proceedings (arXiv:2208.11090), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Robots are increasingly deployed in spaces shared with humans, including home settings and industrial environments. In these environments, the interaction between humans and robots (HRI) is crucial for safety, legibility, and efficiency. A key factor in HRI is trust, which modulates the acceptance of the system. Anthropomorphism has been shown to modulate trust development in a robot, but robots in industrial environments are not usually anthropomorphic. We designed a simple interaction in an industrial environment in which an anthropomorphic mock driver (ARMoD) robot simulates to drive an autonomous guided vehicle (AGV). The task consisted of a human crossing paths with the AGV, with or without the ARMoD mounted on the top, in a narrow corridor. The human and the system needed to negotiate trajectories when crossing paths, meaning that the human had to attend to the trajectory of the robot to avoid a collision with it. There was a significant increment in the reported trust scores in the condition where the ARMoD was present, showing that the presence of an anthropomorphic robot is enough to modulate the trust, even in limited interactions as the one we present here. 

National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-102773 (URN)10.48550/arXiv.2208.14637 (DOI)
Conference
31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022
Projects
DARKO
Funder
EU, Horizon 2020, 101017274 754285
Available from: 2022-12-19 Created: 2022-12-19 Last updated: 2022-12-20Bibliographically approved
Schreiter, T., Almeida, T. R., Zhu, Y., Gutiérrez Maestro, E., Morillo-Mendez, L., Rudenko, A., . . . Lilienthal, A. (2022). The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized. In: : . Paper presented at 31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022.
Open this publication in new window or tab >>The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
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2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment. 

Keywords
Dataset, Human Motion Prediction, Eye Tracking
National Category
Computer Sciences
Identifiers
urn:nbn:se:oru:diva-102772 (URN)10.48550/arXiv.2208.14925 (DOI)
Conference
31st IEEE International Conference on Robot & Human Interactive Communication, Naples, Italy, August 29 - September 2, 2022
Projects
DARKO
Funder
EU, Horizon 2020, 101017274Knut and Alice Wallenberg Foundation
Available from: 2022-12-19 Created: 2022-12-19 Last updated: 2022-12-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9387-2312

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