KnowRob

Representations for robot knowledge in the KnowRob framework. In order to robustly perform tasks based on abstract instructions, robots need sophisticated knowledge processing methods. These methods have to supply the difference between the (often shallow and symbolic) information in the instructions and the (detailed, grounded and often real-valued) information needed for execution. For filling these information gaps, a robot first has to identify them in the instructions, reason about suitable information sources, and combine pieces of information from different sources and of different structure into a coherent knowledge base. To this end we propose the {sc KnowRob} knowledge processing system for robots. In this article, we discuss why the requirements of a robot knowledge processing system differ from what is commonly investigated in AI research, and propose to re-consider a KR system as a semantically annotated view on information and algorithms that are often already available as part of the robot’s control system. We then introduce representational structures and a common vocabulary for representing knowledge about robot actions, events, objects, environments, and the robot’s hardware as well as inference procedures that operate on this common representation. The {sc KnowRob} system has been released as open-source software and is being used on several robots performing complex object manipulation tasks. We evaluate it through prototypical queries that demonstrate the expressive power and its impact on the robot’s performance.


References in zbMATH (referenced in 10 articles , 1 standard article )

Showing results 1 to 10 of 10.
Sorted by year (citations)

  1. Homem, Thiago Pedro Donadon; Santos, Paulo Eduardo; Reali Costa, Anna Helena; da Costa Bianchi, Reinaldo Augusto; Lopez de Mantaras, Ramon: Qualitative case-based reasoning and learning (2020)
  2. Cozman, Fabio Gagliardi; Mauá, Denis Deratani: The finite model theory of Bayesian network specifications: descriptive complexity and zero/one laws (2019)
  3. Cozman, Fabio G.; Mauá, Denis D.: The complexity of Bayesian networks specified by propositional and relational languages (2018)
  4. Hanheide, Marc; Göbelbecker, Moritz; Horn, Graham S.; Pronobis, Andrzej; Sjöö, Kristoffer; Aydemir, Alper; Jensfelt, Patric; Gretton, Charles; Dearden, Richard; Janicek, Miroslav; Zender, Hendrik; Kruijff, Geert-Jan; Hawes, Nick; Wyatt, Jeremy L.: Robot task planning and explanation in open and uncertain worlds (2017)
  5. Kunze, Lars; Beetz, Michael: Envisioning the qualitative effects of robot manipulation actions using simulation-based projections (2017)
  6. Rajan, Kanna; Saffiotti, Alessandro: Editorial: Towards a science of integrated AI and robotics (2017)
  7. Ramirez-Amaro, Karinne; Beetz, Michael; Cheng, Gordon: Transferring skills to humanoid robots by extracting semantic representations from observations of human activities (2017)
  8. Tenorth, Moritz; Beetz, Michael: Representations for robot knowledge in the \textscKnowRobframework (2017)
  9. Nitti, Davide; De Laet, Tinne; De Raedt, Luc: Probabilistic logic programming for hybrid relational domains (2016)
  10. Bandouch, Jan; Jenkins, Odest Chadwicke; Beetz, Michael: A self-training approach for visual tracking and recognition of complex human activity patterns (2012) ioport