To Örebro University

oru.seÖrebro University Publications
Change search
Link to record
Permanent link

Direct link
Publications (7 of 7) Show all publications
Schreiter, T., Rudenko, A., Magnusson, M. & Lilienthal, A. (2024). Human Gaze and Head Rotation during Navigation, Exploration and Object Manipulation in Shared Environments with Robots. In: 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN): . Paper presented at 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), Passadena, CA, USA, 26-30 Aug. 2024 (pp. 1258-1265). IEEE Computer Society
Open this publication in new window or tab >>Human Gaze and Head Rotation during Navigation, Exploration and Object Manipulation in Shared Environments with Robots
2024 (English)In: 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), IEEE Computer Society, 2024, p. 1258-1265Conference paper, Published paper (Refereed)
Abstract [en]

The human gaze is an important cue to signal intention, attention, distraction, and the regions of interest in the immediate surroundings. Gaze tracking can transform how robots perceive, understand, and react to people, enabling new modes of robot control, interaction, and collaboration. In this paper, we use gaze tracking data from a rich dataset of human motion (THÖR-MAGNI) to investigate the coordination between gaze direction and head rotation of humans engaged in various indoor activities involving navigation, interaction with objects, and collaboration with a mobile robot. In particular, we study the spread and central bias of fixations in diverse activities and examine the correlation between gaze direction and head rotation. We introduce various human motion metrics to enhance the understanding of gaze behavior in dynamic interactions. Finally, we apply semantic object labeling to decompose the gaze distribution into activity-relevant regions.

Place, publisher, year, edition, pages
IEEE Computer Society, 2024
Keywords
Adversarial machine learning, Behavioral research, Human engineering, Human robot interaction, Industrial robots, Machine Perception, Microrobots, Motion tracking, Gaze direction, Gaze-tracking, Head rotation, Human motions, Indoor activities, Object manipulation, Region-of-interest, Regions of interest, Robots control, Tracking data, Mobile robots
National Category
Human Computer Interaction
Identifiers
urn:nbn:se:oru:diva-118537 (URN)10.1109/RO-MAN60168.2024.10731190 (DOI)001348918600163 ()2-s2.0-85206976290 (Scopus ID)9798350375022 (ISBN)
Conference
2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), Passadena, CA, USA, 26-30 Aug. 2024
Funder
EU, Horizon 2020, 101017274
Available from: 2025-01-15 Created: 2025-01-15 Last updated: 2025-01-20Bibliographically approved
Schreiter, T., Almeida, T. R., Zhu, Y., Gutiérrez Maestro, E., Morillo-Mendez, L., Rudenko, A., . . . Lilienthal, A. J. (2024). THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction. The international journal of robotics research
Open this publication in new window or tab >>THÖR-MAGNI: A large-scale indoor motion capture recording of human movement and robot interaction
Show others...
2024 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176Article in journal (Refereed) Epub ahead of print
Abstract [en]

We present a new large dataset of indoor human and robot navigation and interaction, called THÖR-MAGNI, that is designed to facilitate research on social human navigation: for example, modeling and predicting human motion, analyzing goal-oriented interactions between humans and robots, and investigating visual attention in a social interaction context. THÖR-MAGNI was created to fill a gap in available datasets for human motion analysis and HRI. This gap is characterized by a lack of comprehensive inclusion of exogenous factors and essential target agent cues, which hinders the development of robust models capable of capturing the relationship between contextual cues and human behavior in different scenarios. Unlike existing datasets, THÖR-MAGNI includes a broader set of contextual features and offers multiple scenario variations to facilitate factor isolation. The dataset includes many social human–human and human–robot interaction scenarios, rich context annotations, and multi-modal data, such as walking trajectories, gaze-tracking data, and lidar and camera streams recorded from a mobile robot. We also provide a set of tools for visualization and processing of the recorded data. THÖR-MAGNI is, to the best of our knowledge, unique in the amount and diversity of sensor data collected in a contextualized and socially dynamic environment, capturing natural human–robot interactions.

Place, publisher, year, edition, pages
Sage Publications, 2024
Keywords
Dataset for human motion, human trajectory prediction, human–robot collaboration, social HRI, human-aware motion planning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-117044 (URN)10.1177/02783649241274794 (DOI)001337861800001 ()2-s2.0-85206993138 (Scopus ID)
Funder
Knut and Alice Wallenberg FoundationEU, Horizon 2020, 101017274
Available from: 2024-10-25 Created: 2024-10-25 Last updated: 2025-01-20Bibliographically approved
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
Show others...
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 graphics and computer vision
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: 2025-02-07Bibliographically 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
Show others...
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 and automation
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: 2025-02-09Bibliographically 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: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW): . Paper presented at IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Paris, France, October 2-6, 2023 (pp. 2192-2201). IEEE
Open this publication in new window or tab >>THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction
Show others...
2023 (English)In: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE, 2023, p. 2192-2201Conference 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.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE International Conference on Computer Vision Workshop (ICCVW), ISSN 2473-9936, E-ISSN 2473-9944
National Category
Computer graphics and computer vision
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-109508 (URN)10.1109/ICCVW60793.2023.00234 (DOI)001156680302028 ()2-s2.0-85182932549 (Scopus ID)9798350307450 (ISBN)9798350307443 (ISBN)
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: 2025-02-07Bibliographically 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
Show others...
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
Show others...
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

Search in DiVA

Show all publications