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  • 1.
    Arriola-Rios, Veronica E.
    et al.
    Department of Mathematics, Faculty of Science, UNAM Universidad Nacional Autonoma de Mexico, Ciudad de México, Mexico.
    Güler, Püren
    Örebro University, School of Science and Technology.
    Ficuciello, Fanny
    PRISMA Laboratory, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
    Kragic, Danica
    Robotics, Learning and Perception Laboratory, Centre for Autonomous Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden.
    Siciliano, Bruno
    PRISMA Laboratory, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy.
    Wyatt, Jeremy L.
    School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
    Modeling of Deformable Objects for Robotic Manipulation: A Tutorial and Review2020In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 7, article id 82Article, review/survey (Refereed)
    Abstract [en]

    Manipulation of deformable objects has given rise to an important set of open problems in the field of robotics. Application areas include robotic surgery, household robotics, manufacturing, logistics, and agriculture, to name a few. Related research problems span modeling and estimation of an object's shape, estimation of an object's material properties, such as elasticity and plasticity, object tracking and state estimation during manipulation, and manipulation planning and control. In this survey article, we start by providing a tutorial on foundational aspects of models of shape and shape dynamics. We then use this as the basis for a review of existing work on learning and estimation of these models and on motion planning and control to achieve desired deformations. We also discuss potential future lines of work.

  • 2.
    Brandão, Martim
    et al.
    Department of Informatics, King's College London, London, United Kingdom.
    Mansouri, Masoumeh
    School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Editorial: Responsible Robotics2022In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 937612Article in journal (Refereed)
  • 3.
    Buyukgoz, Sera
    et al.
    SoftBank Robotics Europe, Paris, France; Sorbonne University, Institute for Intelligent Systems and Robotics, CNRS UMR, Paris, France.
    Grosinger, Jasmin
    Örebro University, School of Science and Technology.
    Chetouani, Mohamed
    Sorbonne University, Institute for Intelligent Systems and Robotics, CNRS UMR, Paris, France.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Two ways to make your robot proactive: Reasoning about human intentions or reasoning about possible futures2022In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 929267Article in journal (Refereed)
    Abstract [en]

    Robots sharing their space with humans need to be proactive to be helpful. Proactive robots can act on their own initiatives in an anticipatory way to benefit humans. In this work, we investigate two ways to make robots proactive. One way is to recognize human intentions and to act to fulfill them, like opening the door that you are about to cross. The other way is to reason about possible future threats or opportunities and to act to prevent or to foster them, like recommending you to take an umbrella since rain has been forecast. In this article, we present approaches to realize these two types of proactive behavior. We then present an integrated system that can generate proactive robot behavior by reasoning on both factors: intentions and predictions. We illustrate our system on a sample use case including a domestic robot and a human. We first run this use case with the two separate proactive systems, intention-based and prediction-based, and then run it with our integrated system. The results show that the integrated system is able to consider a broader variety of aspects that are required for proactivity.

  • 4.
    Güler, Püren
    et al.
    Örebro University, Örebro, Sweden.
    Stork, Johannes A.
    Örebro University, School of Science and Technology.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Visual state estimation in unseen environments through domain adaptation and metric learning2022In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 833173Article in journal (Refereed)
    Abstract [en]

    In robotics, deep learning models are used in many visual perception applications, including the tracking, detection and pose estimation of robotic manipulators. The state of the art methods however are conditioned on the availability of annotated training data, which may in practice be costly or even impossible to collect. Domain augmentation is one popular method to improve generalization to out-of-domain data by extending the training data set with predefined sources of variation, unrelated to the primary task. While this typically results in better performance on the target domain, it is not always clear that the trained models are capable to accurately separate the signals relevant to solving the task (e.g., appearance of an object of interest) from those associated with differences between the domains (e.g., lighting conditions). In this work we propose to improve the generalization capabilities of models trained with domain augmentation by formulating a secondary structured metric-space learning objective. We concentrate on one particularly challenging domain transfer task-visual state estimation for an articulated underground mining machine-and demonstrate the benefits of imposing structure on the encoding space. Our results indicate that the proposed method has the potential to transfer feature embeddings learned on the source domain, through a suitably designed augmentation procedure, and on to an unseen target domain.

  • 5.
    Kyvik Nordås, Hildegunn
    et al.
    Örebro University, Örebro University School of Business. Council on Economic Policies (CEP), Zürich, Switzerland.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Drivers of Automation and Consequences for Jobs in Engineering Services: An Agent-Based Modelling Approach2021In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 8, article id 637125Article in journal (Refereed)
    Abstract [en]

    New technology is of little use if it is not adopted, and surveys show that less than 10% of firms use Artificial Intelligence. This paper studies the uptake of AI-driven automation and its impact on employment, using a dynamic agent-based model (ABM). It simulates the adoption of automation software as well as job destruction and job creation in its wake. There are two types of agents: manufacturing firms and engineering services firms. The agents choose between two business models: consulting or automated software. From the engineering firms' point of view, the model exhibits static economies of scale in the software model and dynamic (learning by doing) economies of scale in the consultancy model. From the manufacturing firms' point of view, switching to the software model requires restructuring of production and there are network effects in switching. The ABM matches engineering and manufacturing agents and derives employment of engineers and the tasks they perform, i.e. consultancy, software development, software maintenance, or employment in manufacturing. We find that the uptake of software is gradual; slow in the first few years and then accelerates. Software is fully adopted after about 18 years in the base line run. Employment of engineers shifts from consultancy to software development and to new jobs in manufacturing. Spells of unemployment may occur if skilled jobs creation in manufacturing is slow. Finally, the model generates boom and bust cycles in the software sector.

  • 6.
    Mansouri, Masoumeh
    et al.
    Intelligent Robotics Lab, School of Computer Science, University of Birmingham, Birmingham, United Kingdom.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Schüller, Peter
    Knowledge-Based Systems Group, TU Wien, Vienna, Austria.
    Combining Task and Motion Planning: Challenges and Guidelines2021In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 8, article id 637888Article in journal (Refereed)
    Abstract [en]

    Combined Task and Motion Planning (TAMP) is an area where no one-fits-all solution can exist. Many aspects of the domain, as well as operational requirements, have an effect on how algorithms and representations are designed. Frequently, trade-offs have to be madet o build a system that is effective. We propose five research questions that we believe need to be answered to solve real-world problems that involve combined TAMP. We show which decisions and trade-offs should be made with respect to these research questions, and illustrate these on examples of existing application domains. By doing so, this article aims to provide a guideline for designing combined TAMP solutions that are adequate and effective in the target scenario.

  • 7.
    Persson, Andreas
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Learning Actions to Improve the Perceptual Anchoring of Object2017In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 3, no 76Article in journal (Refereed)
    Abstract [en]

    In this paper, we examine how to ground symbols referring to objects in perceptual data from a robot system by examining object entities and their changes over time. In particular, we approach the challenge by 1) tracking and maintaining object entities over time; and 2) utilizing an artificial neural network to learn the coupling between words referring to actions and movement patterns of tracked object entities. For this purpose, we propose a framework which relies on the notations presented in perceptual anchoring. We further present a practical extension of the notation such that our framework can track and maintain the history of detected object entities. Our approach is evaluated using everyday objects typically found in a home environment. Our object classification module has the possibility to detect and classify over several hundred object categories. We demonstrate how the framework creates and maintains, both in space and time, representations of objects such as 'spoon' and 'coffee mug'. These representations are later used for training of different sequential learning algorithms in order to learn movement actions such as 'pour' and 'stir'. We finally exemplify how learned movements actions, combined with common-sense knowledge, further can be used to improve the anchoring process per se.

  • 8.
    Swaminathan, Chittaranjan Srinivas
    et al.
    Örebro University, School of Science and Technology.
    Kucner, Tomasz Piotr
    Finnish Centre for Artificial Intelligence (FCAI), Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Palmieri, Luigi
    Robert Bosch GmbH Corporate Research, Stuttgart, Germany.
    Molina, Sergi
    Lincoln Centre for Autonomous Systems, School of Computer Science, University of Lincoln, Lincoln, United Kingdom.
    Mannucci, Anna
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Benchmarking the utility of maps of dynamics for human-aware motion planning2022In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 9, article id 916153Article in journal (Refereed)
    Abstract [en]

    Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.

    Download full text (pdf)
    Benchmarking the utility of maps of dynamics for human-aware motion planning
  • 9.
    Zuidberg Dos Martires, Pedro
    et al.
    Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Kumar, Nitesh
    Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Persson, Andreas
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    De Raedt, Luc
    Örebro University, School of Science and Technology. Declaratieve Talen en Artificiele Intelligentie (DTAI), Department of Computer Science, KU Leuven, Leuven, Belgium.
    Symbolic Learning and Reasoning With Noisy Data for Probabilistic Anchoring2020In: Frontiers in Robotics and AI, E-ISSN 2296-9144, Vol. 7, article id 100Article in journal (Refereed)
    Abstract [en]

    Robotic agents should be able to learn from sub-symbolic sensor data and, at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.

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