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  • 1.
    Hertzberg, Joachim
    et al.
    Osnabrück University, Osnabrück, Germany .
    Zhang, Jianwei
    Hamburg University, Hamburg, Germany .
    Zhang, Liwei
    Hamburg University, Hamburg, Germany .
    Rockel, Sebastian
    Hamburg University, Hamburg, Germany .
    Neumann, Bernd
    Hamburg University, Hamburg, Germany .
    Lehmann, Jos
    Hamburg University, Hamburg, Germany .
    Dubba, Krishna S.R.
    University of Leeds, Leeds, England .
    Cohn, Anthony G.
    University of Leeds, Leeds, England .
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Mansouri, Masoumeh
    Örebro University, School of Science and Technology.
    Konečný, Štefan
    Örebro University, School of Science and Technology.
    Günther, Martin
    Osnabrück University, Osnabrück, Germany .
    Stock, Sebastian
    Osnabrück University, Osnabrück, Germany .
    Seabra Lopes, Luis
    University of Aveiro, Aveiro, Portugal .
    Oliveira, Miguel
    University of Aveiro, Aveiro, Portugal .
    Lim, Gi Hyun
    University of Aveiro, Aveiro, Portugal .
    Kasaei, Hamidreza
    University of Aveiro, Aveiro, Portugal .
    Mokhtari, Vahid
    University of Aveiro, Aveiro, Portugal .
    Hotz, Lothar
    HITeC Hamburger Informatik Technologie-Center e. V., Hamburg, Germany .
    Bohlken, Wilfried
    HITeC Hamburger Informatik Technologie-Center e. V., Hamburg, Germany .
    The RACE Project: Robustness by Autonomous Competence Enhancement2014In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 28, no 4, p. 297-304Article in journal (Refereed)
    Abstract [en]

    This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.

  • 2.
    Konečný, Štefan
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Execution Knowledge for Execution Monitoring: what, why, where and what for?2014In: IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS), 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Despite the progress made in planning androbotics, autonomous plan execution on a robot remainschallenging. One of the problems is that (classical) plannersuse abstract models which are disconnected from the sensorand actuation information available during execution. Thisconnection is typically created in a non-systematic way by somesystem-specific execution software. In this paper we proposeto explicitly represent Execution Knowledge that encodes theconnection between planning models and the actual actionsand observations for a given physical system. We present anexecution monitoring framework in which Execution Knowl-edge captures the expectations about physical plan execution.A violation of these expectations indicates an execution failure.

    Download full text (pdf)
    Execution Knowledge
  • 3.
    Konečný, Štefan
    et al.
    Örebro University, School of Science and Technology.
    Stock, Sebastian
    Osnabrück University, Osnabrück, Germany.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Planning domain + execution semantics: a way towards robust execution?2014In: Qualitative Representations for Robots: Papers from the AAAI Spring Symposium, AAAI Press , 2014Conference paper (Refereed)
    Abstract [en]

    Robots are expected to carry out complex plans in real world environments. This requires the robot to track the progress of plan execution and detect failures which may occur. Planners use very abstract world models to generate plans. Additional causal, temporal, categorical knowledge about the execution, which is not included in the planner's model, is often available. Can we use this knowledge to increase robustness of execution and provide early failure detection? We propose to use a dedicated Execution Model to monitor the executed plan based on runtime observations and rich execution knowledge. We show that the combined used of causal, temporal and categorical knowledge allows the robot to detect failures even when the effects of actions are not directly observable. A dedicated Execution model also introduces a degree of modularity, since the platform- and execution-specific knowledge does not need to be encoded into the planner.

  • 4.
    Rockel, S.
    et al.
    University of Hamburg, Hamburg, Germany.
    Neumann, B.
    University of Hamburg, Hamburg, Germany.
    Zhang, J.
    University of Hamburg, Hamburg, Germany.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Konečný, Štefan
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Tomé, A.M.
    University of Aveiro, Aveiro, Portugal.
    Pinho, A.
    University of Aveiro, Aveiro, Portugal.
    Sebra Lopes, L.
    University of Aveiro, Aveiro, Portugal.
    Dubba, K.S.R.
    University of Leeds, Leeds, UK.
    Cohn, A.G.
    University of Leeds, Leeds, UK.
    Günther, M.
    University of Osnabrück, Osnabrück, Germany.
    Stock, S.
    University of Osnabrück, Osnabrück, Germany.
    Hertzberg, J.
    University of Osnabrück, Osnabrück, Germany.
    von Riegen, S.
    HITeC e.V., Hamburg, Germany.
    Hotz, L.
    HITeC e.V., Hamburg, Germany.
    An ontology-based multi-level robot architecture for learning from experiences2013In: Designing intelligent robots: reintegrating AI: Papers from the AAAI Spring Symposium, AAAI Press, 2013, p. 52-57Conference paper (Refereed)
  • 5.
    Rockel, Sebastian
    et al.
    University of Hamburg, Hamburg, Germany.
    Konečný, Štefan
    Örebro University, School of Science and Technology.
    Stock, Sebastian
    Osnabrück University, Osnabrück, Germany; DFKI Robotics Innovation Center, Osnabrück, Germany.
    Hertzberg, Joachim
    Osnabrück University, Osnabrück, Germany; DFKI Robotics Innovation Center, Osnabrück, Germany.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Zhang, Jianwei
    University of Hamburg, Hamburg, Germany.
    Integrating physics-based prediction with semantic plan execution monitoring2015In: 2015 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), IEEE , 2015, p. 2883-2888Conference paper (Refereed)
    Abstract [en]

    Real-world robotic systems have to deal with uncertain and dynamic environments to reliably perform tasks. State-of-the-art cognitive robotic systems use an abstract symbolic representation of the real world that is used for high level reasoning. Some aspects of the world, such as object dynamics, are inherently difficult to capture in an abstract symbolic form, yet they influence whether the executed action will succeed or fail. This paper presents an integrated system that uses a physics-based simulation for predicting robot action results and durations, combined with a Hierarchical Task Network (HTN) planner and semantic execution monitoring. We describe a fully integrated system performing functional imagination, which is essentially contributed by a Semantic Execution Monitor (SEM). Based on information obtained from functional imagination, the robot control decides whether it is necessary to adapt the plan that is currently being executed. As a proof of concept, we demonstrate PR2 able of carrying objects on a tray without the objects toppling. Our approach achieves this by considering the robot and object dynamics in simulation. A validation shows that robot action results in simulation can be transferred to the real world. The system improves on state-of-the-art AI plan-based systems by feeding simulated prediction results back into the execution system.

1 - 5 of 5
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