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International Journal of Social Robotics

, Volume 7, Issue 4, pp 421–438 | Cite as

The Role of Functional Affordances in Socializing Robots

  • Iman Awaad
  • Gerhard K. Kraetzschmar
  • Joachim Hertzberg
Article

Abstract

Just as humans behave according to the social norms of their groups, autonomous systems that become part of these groups also need to behave in socially-expected and accepted ways. For humans these social norms are learned through interaction with members of the group. In this work, we propose that the functional affordances of objects, what objects are meant to be used for, provide us with a starting point for the socialization of such agents. We model these functional affordances in description logics and show how this enables the socially-expected human behavior of substituting objects as needed to achieve a goal. In addition, we propose to combine these affordances with conceptual similarity and proximity in order to make more complex substitutions, which are socially acceptable in their given context. Finally, we describe how their use would allow the agent to take advantage of opportunities and how they are modified and extended through interaction with humans.

Keywords

Social intelligence Affordances Robotics Plan-based robot control Reasoning 

Notes

Acknowledgments

The authors thank Elizaveta Shpieva, Christian Tiefenau, Daniel Höller and Sven Schneider for their help in implementing some of the ideas presented here. The authors also thank Sven Schneider and Anastassia Küstenmacher for the many useful discussions. Parts of this publication have been previously published in [4, 5]. Iman Awaad gratefully acknowledges financial support provided by a PhD scholarship from the Graduate Institute of Bonn-Rhein-Sieg University.

References

  1. 1.
    Andrighetto G, Governatori G, Noriega P, van der Torre LWN (eds.) (2013) Normative Multi-Agent Systems. Dagstuhl Follow-Ups, Dagstuhl Follow-Ups, vol. 4. Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik, Dagstuhl, GermanyGoogle Scholar
  2. 2.
    Arkin RC (1998) Behavior-Based Robotics. Intelligent Robots and Autonomous Agents. MIT-Press, Cambridge, MA, USAGoogle Scholar
  3. 3.
    Awaad I, Kraetzschmar GK, Hertzberg J (2013) Affordance-based reasoning in robot task planning. In: Planning and Robotics (PlanRob) Workshop at 23rd International Conference on Automated Planning and Scheduling (ICAPS)Google Scholar
  4. 4.
    Awaad I, Kraetzschmar GK, Hertzberg J (2013) Socializing robots: The role of functional affordances. In: International Workshop on Developmental Social Robotics (DevSoR): Reasoning about Human, Perspective, Affordances and Effort for Socially Situated Robots at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Google Scholar
  5. 5.
    Awaad I, Kraetzschmar GK, Hertzberg J (2014) Finding ways to get the job done: An affordance-based approach. In: Proceedings of the 24th International Conference on Planning and Scheduling (ICAPS)Google Scholar
  6. 6.
    Beetz M, Hertzberg J, Ghallab M, Pollack ME (eds.) (2002) Advances in Plan-Based Control of Robotic Agents, International Seminar, Dagstuhl Castle, Germany, Lecture Notes in Computer Science, vol. 2466. SpringerGoogle Scholar
  7. 7.
    Bradshaw JM, Feltovich PJ, Johnson M (2011) The handbook of human-machine interaction: a human-centered design approach, chap. 13. Farnham, Surrey, England; Burlington, VT: Ashgate, pp. 283–300Google Scholar
  8. 8.
  9. 9.
    Delaitre V, Sivic J, Laptev I (2011) Learning person-object interactions for action recognition in still images. In: Advances in Neural Information Processing Systems (NIPS)Google Scholar
  10. 10.
    Diankov R (2010) Automated Construction of Robotic Manipulation Programs. Ph.D. thesis, Carnegie Mellon University, Robotics InstituteGoogle Scholar
  11. 11.
    Erol K, Hendler J, Nau DS (1994) HTN planning: Complexity and expressivity. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94). AAAI Press, pp. 1123–1128Google Scholar
  12. 12.
    Field T (2011) SMACH documentation. Online at http://www.ros.org/wiki/smach/Documentation
  13. 13.
    Fitzpatrick P, Metta G, Natale L, Rao S, Sandini G (2003) Learning about objects through action - initial steps towards artificial cognition. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3140–3145Google Scholar
  14. 14.
    Fritz G, Paletta L, Dorffner G, Breithaupt R, Rome E (2006) Learning predictive features in affordance based robotic perception systems. In: Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on, pp. 3642–3647Google Scholar
  15. 15.
    Gärdenfors P (2004) How to Make the Semantic Web More Semantic. In: Proceedings of the Third International Conference on Formal Ontology in Information Systems (FOIS 2004), pp. 17–34Google Scholar
  16. 16.
    Gärdenfors P, Warglien M (2012) Using Conceptual Spaces to Model Actions and Events. Journal of Semantics 29(4):487–519CrossRefzbMATHGoogle Scholar
  17. 17.
    Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, BostonGoogle Scholar
  18. 18.
    Graf B, Reiser U, Hägele M, Mauz K, Klein P (2009) Robotic Home Assistant Care-O-bot 3 - Product Vision and Innovation Platform. In: Advanced Robotics and its Social Impacts (ARSO), 2009 IEEE Workshop on, pp. 139–144 doi: 10.1109/ARSO.2009.5587059
  19. 19.
    Group AR Atlas transformation language. Online at http://www.eclipse.org/atl/
  20. 20.
    Hartanto R (ed) (2011) A Hybrid Deliberative Layer for Robotic Agents: Fusing DL Reasoning with HTN Planning in Autonomous Robots. Springer-Verlag, Berlin, HeidelbergGoogle Scholar
  21. 21.
    Hartson HR (2003) Cognitive, physical, sensory, and functional affordances in interaction design. Behaviour & IT 22(5):315–338Google Scholar
  22. 22.
    Hermans T, Rehg JM, Bobick A (2011) Affordance prediction via learned object attributes. In: Workshop on Semantic Perception, Mapping, and Exploration at the IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  23. 23.
    Höller D (2013) Affordance-based action abstraction in robot planning. Master’s thesis, Bonn-Rhein-Sieg University of Applied SciencesGoogle Scholar
  24. 24.
    Holt JC (1964) How Children Fail. PitmanGoogle Scholar
  25. 25.
    Hubel N, Mohanarajah G, van de Molengraft R, Waibel M, D’Andrea R (2010) RoboEarth Project. Online at http://www.RoboEarth.org
  26. 26.
    Hutchins E (1995) Cognition in the wild. MIT Press, Cambridge, MAGoogle Scholar
  27. 27.
    Ilghami O, Nau DS (2003) A General Approach to Synthesize Problem-Specific Planners. Tech. Rep. CS-TR-4597, UMIACS-TR-2004-40, University of MarylandGoogle Scholar
  28. 28.
    Janowicz K, Raubal M (2007) Affordance-based similarity measurement for entity types. In: Winter S, Duckham M, Kulik L, Kuipers B (eds) Spatial Information Theory, vol 4736. Springer-Verlag, Berlin Heidelberg, pp 133–151CrossRefGoogle Scholar
  29. 29.
    Koppula HS, Saxena A (2013) Anticipating human activities using object affordances for reactive robotic response. In: Proceedings of Robotics: Science and Systems (RSS)Google Scholar
  30. 30.
    Kraft D, Detry R, Pugeault N, Baseski E, Piater JH, Krüger N (2009) Learning objects and grasp affordances through autonomous exploration. In: Fritz M, Schiele B, Piater JH (eds) Computer Vision Systems, Lecture Notes in Computer Science, vol. 5815, Springer, pp. 235–244Google Scholar
  31. 31.
    Levihn M, Kaelbling LP, Lozano-Perez T, Stilman M (2013) Foresight and reconsideration in hierarchical planning and execution. In: Workshop on Cognitive Assistive Systems at the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Google Scholar
  32. 32.
    Mason M, Lopes MC (2011) Robot self-initiative and personalization by learning through repeated interactions. In: Proceedings of the 6th International Conference on Human-robot Interaction, HRI ’11. ACM, New York, NY, USA, pp 433–440Google Scholar
  33. 33.
    McKean E (ed) (2005) The New Oxford American Dictionary. Oxford University Press,Google Scholar
  34. 34.
    Meneguzzi F, De Silva L (2015) Planning in bdi agents: a survey of the integration of planning algorithms and agent reasoning. The Knowledge Engineering Review 30:1–44CrossRefGoogle Scholar
  35. 35.
    Moldovan B, Otterlo MV, Lopez PM, Santos-Victor J, Raedt LD (2011) Statistical relational learning of object affordances for robotic manipulation. In: ILPGoogle Scholar
  36. 36.
    Nelson DGK (1999) Attention to functional properties in toddlers’ naming and problem-solving. Cognitive Development 14(1):77–100CrossRefGoogle Scholar
  37. 37.
    Norman D (2002) The psychology of everyday things. Basic Books, New YorkGoogle Scholar
  38. 38.
    Pandey AK (2012) Towards socially intelligent robots in human centered environment. Ph.D. thesis, University of ToulouseGoogle Scholar
  39. 39.
    Patel M, Ek CH, Kyriazis N, Argyros A, Valls Miro J, Kragic D (2013) Language for learning complex human-object interactions. In: IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  40. 40.
    Peter Bonasso R, James Firby R, Gat E, Kortenkamp D, Miller DP, Slack MG (1997) Experiences with an architecture for intelligent, reactive agents. Journal of Experimental & Theoretical Artificial Intelligence 9(2–3):237–256CrossRefGoogle Scholar
  41. 41.
    Poggi I, D’Errico F (2011) Social signals: A psychological perspective. In: Computer Analysis of Human Behavior. Springer, pp. 185–225Google Scholar
  42. 42.
    Quigley M, Conley K, Gerkey B, Faust J, Foote TB, Leibs J, Wheeler R, Ng AY (2009) ROS: an open-source robot operating system. In: Workshop on Open Source Software at the IEEE International Conference on Robotics and Automation (ICRA)Google Scholar
  43. 43.
    Raubal M (2004) Formalizing conceptual spaces. In: Varzi A, Vieu L (eds) Proceedings of the 3rd International Conference on Formal Ontology in Information Systems (FOIS 2004). Torino, Italy, pp 153–164Google Scholar
  44. 44.
    Raubal M, Moratz R (2008) A functional model for affordance-based agents. In: Rome E, Hertzberg J, Dorffner G (eds) Towards Affordance-Based Robot Control, Lecture Notes in Computer Science, vol 4760. Springer-Verlag, Berlin, Heidelberg, pp 91–105CrossRefGoogle Scholar
  45. 45.
    Reno RR, Cialdini RB, Kallgren CA (1993) The transsituational influence of social norms. Journal of Personality and Social Psychology 64Google Scholar
  46. 46.
    Ridge B, Skocaj D, Leonardis A (2010) Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems. In: IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 5047–5054Google Scholar
  47. 47.
    Roberts M, Howe A, Ray I (2014) Evaluating diversity in classical planning. In: Proccedings of the 24th International Conference on Planning and Scheduling (ICAPS)Google Scholar
  48. 48.
    Rockel S, Neumann B, Zhang J, Dubba K, Cohn A, Konecny S, Mansouri M, Pecora F, Saffiotti A, Günther M, Stock S, Hertzberg J, Tome A, Pinho A, Lopes LS, von Riegen S, Hotz L (2013) An ontology-based multi-level robot architecture for learning from experiences. In: Designing intelligent robots: reintegrating AI IIGoogle Scholar
  49. 49.
    Ros R, Lemaignan S, Sisbot EA, Alami R, Steinwender J, Hamann K, Warneken F (2010) Which one? grounding the referent based on efficient human-robot interaction. In: 19th IEEE International Symposium in Robot and Human Interactive CommunicationGoogle Scholar
  50. 50.
    Russell S, Norvig P (2003) Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall,Google Scholar
  51. 51.
    Sapir E (1921) Language: An introduction to the study of speech. Harcourt, Brace and company, New YorkGoogle Scholar
  52. 52.
    Schneider S (2013) Design of a declarative language for task-oriented grasping and tool-use with dextrous robotic hands. Master’s thesis, Bonn-Rhein-Sieg University of Applied Sciences, St. Augustin, GermanyGoogle Scholar
  53. 53.
    Severi P, Fiadeiro J, Ekserdjian D (2011) Guiding the representation of n-ary relations in ontologies through aggregation, generalization and participation. Web Semantics: Science, Services and Agents on the World Wide Web 9(2)Google Scholar
  54. 54.
    Shpieva E, Awaad I (2013) Integrating the planning, execution and monitoring systems for a domestic service robot. In: Workshop on Roboterkontrollarchitekturen at InformatikGoogle Scholar
  55. 55.
    Sirin E, Parsia B (2007) SPARQL-DL: SPARQL Query for OWL-DL. In: Proceedings of the Third International Workshop on OWL: Experiences and Directions (OWLED ’07)Google Scholar
  56. 56.
    Stark M, Lies P, Zillich M, Wyatt J, Schiele B (2008) Functional object class detection based on learned affordance cues. 6th International Conference on Computer Vision Systems (ICVS), vol 5008. Springer, Berlin / Heidelberg, Santorini, Greece, pp 435–444Google Scholar
  57. 57.
    Steedman M (2002) Plans, affordances, and combinatory grammar. Linguistics and Philosophy 25Google Scholar
  58. 58.
    Sun J (2008) Object categorization for affordance prediction. Ph.D. thesis, Georgia Institute of TechnologyGoogle Scholar
  59. 59.
    Tenorth M, Beetz M (2009) KnowRob - knowledge processing for autonomous personal robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4261–4266Google Scholar
  60. 60.
    The Eclipse Foundation: Eclipse Modeling Framework Project Core. Online at http://www.eclipse.org/modeling/emf/?project=emf (2013)
  61. 61.
    Ugur E, Sahin E, Oztop E (2009) Predicting future object states using learned affordances. In: ISCIS, pp. 415–419Google Scholar
  62. 62.
    Ugur E, Sahin E, Oztop E (2011) Unsupervised learning of object affordances for planning in a mobile manipulation platform. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4312–4317Google Scholar
  63. 63.
    Varadarajan K, Vincze M (2011) Object part segmentation and classification in range images for grasping. In: 15th International Conference on Advanced Robotics (ICAR), pp. 21–27Google Scholar
  64. 64.
    Zhang J, Patel VL (2006) Distributed cognition, representation, and affordance. Cognition and Pragmatics 14(2):333–341CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  • Iman Awaad
    • 1
  • Gerhard K. Kraetzschmar
    • 1
  • Joachim Hertzberg
    • 2
  1. 1.Bonn-Rhein-Sieg UniversitySankt AugustinGermany
  2. 2.Osnabrück University and DFKI RIC Osnabrück BranchOsnabrückGermany

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