Töberg, Jan-Philipp

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Töberg, Jan-Philipp
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    Item-typ:Veröffentlichung,
    Transforming Web Knowledge into Actionable Knowledge Graphs for Robot Manipulation Tasks
    (RWTH Aachen, 2024)
    Beetz, Michael
    ;
    Cimiano, Philipp
    ;
    Kümpel, Michaela
    ;
    Motta, Enrico
    ;
    Tiddi, Ilaria
    One of the visions in AI based robotics are household robots that can autonomously handle a variety of meal preparation tasks. Based on this scenario, we present a best practice tutorial on how to create actionable knowledge graphs that a robot can use for execution of task variations of cutting actions. We implemented a solution for this task that integrates all necessary software components in the framework of the robot control process. In the context of this tutorial, we focus on knowledge acquisition, knowledge representation and reasoning, and simulating robot action execution, bringing these components together into a learning environment that – in the extended version – introduces the whole control process of Cognitive Robotics. In particular, the Tutorial will detail necessary concepts a knowledge graph should include for robot action execution, how web knowledge can be automatically acquired for the domain of cutting fruits, and how the created knowledge graph can be used to let robots execute tasks like slicing a cucumber or quartering an apple. The learning environment follows an immersive approach, using a physics-based simulation environment for visualization purposes that helps to illustrate the concepts taught in the tutorial.
    Konferenzbeitrag
      29  16
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    Item-typ:Veröffentlichung,
    Towards a Knowledge Engineering Methodology for Flexible Robot Manipulation in Everyday Tasks
    (RWTH Aachen, 2024)
    Kümpel, Michaela
    ;
    Töberg, Jan-Philipp
    ;
    Hassouna, Vanessa
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    Cimiano, Philipp
    ;
    Beetz, Michael
    In the last decade, there have been great advancements in household robotics, enabling robots to autonomously accomplish household tasks. These robots are typically programmed for specific tasks and/ or objects. We hypothesise that the lack of flexibility in fulfilling new ad-hoc task requests can be overcome by a knowledge-based approach, allowing robots to infer how to address a new task or carry out known tasks on new objects. Towards this goal, we propose a knowledge-based methodology that leverages knowledge already existing on the web to construct an ontology supporting robots in reasoning about parameters that influence manipulation actions for execution of task variations on a range of objects. The ontology comprises object and action information, covering dispositions and affordances as well as task-specific properties. As a proof-of-concept, we manually construct a food-cutting ontology by importing and linking knowledge from relevant ontologies in addition to extracting and semantically enhancing knowledge from unstructured web sources. We demonstrate how robots can query the ontology and translate the contained information into action parameters. We evaluate the feasibility of the created ontology by simulating a robot accessing the ontology for parameterisation of actions to perform task variations of cutting.
    text::conference output::conference proceedings::conference paper
      30  14