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  • 1.
    Almeida, Tiago
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Schreiter, Tim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Zhu, Yufei
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Gutiérrez Maestro, Eduardo
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Morillo-Mendez, Lucas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Kucner, Tomasz P.
    Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland; FCAI, Finnish Center for Artificial Intelligence, Finland.
    Martinez Mozos, Oscar
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Arras, Kai O.
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    THÖR-Magni: Comparative Analysis of Deep Learning Models for Role-Conditioned Human Motion Prediction2023Ingår i: 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE, 2023, s. 2192-2201Konferensbidrag (Refereegranskat)
    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.

  • 2.
    Morillo-Mendez, Lucas
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Stower, Rebecca
    Division of Robotics, Perception and Learning, KTH, Stockholm, Sweden.
    Sleat, Alex
    Division of Robotics, Perception and Learning, KTH, Stockholm, Sweden.
    Schreiter, Tim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Leite, Iolanda
    Division of Robotics, Perception and Learning, KTH, Stockholm, Sweden.
    Martinez Mozos, Oscar
    Örebro universitet, Institutionen för naturvetenskap och teknik. Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden.
    Schrooten, Martien G. S.
    Örebro universitet, Institutionen för beteende-, social- och rättsvetenskap.
    Can the robot "see" what I see? Robot gaze drives attention depending on mental state attribution2023Ingår i: Frontiers in Psychology, E-ISSN 1664-1078, Vol. 14, artikel-id 1215771Artikel i tidskrift (Refereegranskat)
    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.

  • 3.
    Schreiter, Tim
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Almeida, Tiago Rodrigues de
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Zhu, Yufei
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Gutiérrez Maestro, Eduardo
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Morillo-Mendez, Lucas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany .
    Kucner, Tomasz P.
    Mobile Robotics Group, Department of Electrical Engineering and Automation, Aalto University, Finland.
    Martinez Mozos, Oscar
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Palmieri, Luigi
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany .
    Arras, Kai O.
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany .
    Lilienthal, Achim
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized2022Konferensbidrag (Refereegranskat)
    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. 

    Ladda ner fulltext (pdf)
    The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
  • 4.
    Schreiter, Tim
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Morillo-Mendez, Lucas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi T.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Billing, Erik
    Interaction Lab, University of Skövde, Skövde, Sweden.
    Magnusson, Martin
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Arras, Kai O.
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik. TU Munich, Germany.
    Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver2023Ingår i: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN): Proceedings, IEEE, 2023, s. 293-300Konferensbidrag (Refereegranskat)
    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.

  • 5.
    Schreiter, Tim
    et al.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Morillo-Mendez, Lucas
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Chadalavada, Ravi Teja
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    Rudenko, Andrey
    Robert Bosch GmbH, Corporate Research, Stuttgart, Germany.
    Billing, Erik Alexander
    Interaction Lab, University of Skövde, Sweden.
    Lilienthal, Achim J.
    Örebro universitet, Institutionen för naturvetenskap och teknik.
    The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction2022Ingår i: SCRITA Workshop Proceedings (arXiv:2208.11090), 2022Konferensbidrag (Refereegranskat)
    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. 

    Ladda ner fulltext (pdf)
    The Effect of Anthropomorphism on Trust in an Industrial Human-Robot Interaction
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