Knowledge Representation and Inference for Grasp Affordances

  • Karthik Mahesh Varadarajan
  • Markus Vincze
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6962)


Knowledge bases for semantic scene understanding and processing form indispensable components of holistic intelligent computer vision and robotic systems. Specifically, task based grasping requires the use of perception modules that are tied with knowledge representation systems in order to provide optimal solutions. However, most state-of-the-art systems for robotic grasping, such as the K- CoPMan, which uses semantic information in mapping and planning for grasping, depend on explicit 3D model representations, restricting scalability. Moreover, these systems lacks conceptual knowledge that can aid the perception module in identifying the best objects in the field of view for task based manipulation through implicit cognitive processing. This restricts the scalability, extensibility, usability and versatility of the system. In this paper, we utilize the concept of functional and geometric part affordances to build a holistic knowledge representation and inference framework in order to aid task based grasping. The performance of the system is evaluated based on complex scenes and indirect queries.


Ontologies Knowledge Representation Grasp Affordances ConceptNet 


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  1. 1.
    Tenorth, M., Beetz, M.: KnowRob — Knowledge Processing for Autonomous Personal Robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2009)Google Scholar
  2. 2.
    Pangercic, D., Tenorth, M., Jain, D., Beetz, M.: Combining Perception and Knowledge Processing for Everyday Manipulation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2010) Google Scholar
  3. 3.
    Raubal, M., Moratz, R.: A Functional Model for Affordance-Based Agents. In: Rome, E., Hertzberg, J., Dorffner, G. (eds.) Towards Affordance-Based Robot Control. LNCS (LNAI), vol. 4760, pp. 91–105. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Barsalou, L., Sloman, S., Chaigneau, S.: The HIPE Theory of Function. In: Carlson, L., van der Zee, E. (eds.) Representing Functional Features for Language and Space: Insights from Perception, Categorization and Development, pp. 131–147. Oxford University Press, New York (2005)Google Scholar
  5. 5.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998)zbMATHGoogle Scholar
  6. 6.
    Havasi, C., Speer, R., Alonso, J.: Conceptnet 3: A Flexible, Multilingual Semantic Network For Common Sense Knowledge. Recent Advances in Natural Language Processing, 27–29 (September 2007)Google Scholar
  7. 7.
    Xiong, X., Hu, Y., Zhang, J.: EpistemeBase: a Semantic Memory System for Task Planning under Uncertainties. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS (2010)Google Scholar
  8. 8.
    Cutkosky, M.R.: On grasp choice, grasp models, and the design of hands for manufacturing tasks. IEEE Transactions on Robotics and Automation 5(3), 269–279 (1989)CrossRefGoogle Scholar
  9. 9.
    Feix, T., Pawlik, R., Schmiedmayer, H., Romero, J., Kragic, D.: A comprehensive grasp taxonomy. In: Robotics, Science and Systems Conference: Workshop on Understanding the Human Hand for Advancing Robotic Manipulation, Poster Presentation (June 2009)Google Scholar
  10. 10.
    Hofman, I., Jarvis, R.: Object Recognition Via Attributed Graph Matching. In: Australasian Conference on Robotics and Automation, ACRA (2000)Google Scholar
  11. 11.
    Jouili, S., Mili, I., Tabbone, S.: Attributed graph matching using local descriptions. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 89–99. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Karthik Mahesh Varadarajan
    • 1
  • Markus Vincze
    • 1
  1. 1.Automation and Control InstituteTU ViennaAustria

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