Skip to main content
Log in

The Role of Functional Affordances in Socializing Robots

  • Published:
International Journal of Social Robotics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. The JSHOP2 planner allows alternative method decompositions to be specified as branches. Each branch has a set of preconditions which need to be met for a particular decomposition to be applicable. This is a convenient shortcut which results in an ‘if, elseif’ structure.

References

  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, Germany

  2. Arkin RC (1998) Behavior-Based Robotics. Intelligent Robots and Autonomous Agents. MIT-Press, Cambridge, MA, USA

    Google Scholar 

  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)

  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)

  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)

  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. Springer

  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. 283–300

  8. Cycorp: OpenCyc. Online at http://www.opencyc.org/ (http://www.opencyc.org/)

  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)

  10. Diankov R (2010) Automated Construction of Robotic Manipulation Programs. Ph.D. thesis, Carnegie Mellon University, Robotics Institute

  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. 1123–1128

  12. Field T (2011) SMACH documentation. Online at http://www.ros.org/wiki/smach/Documentation

  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. 3140–3145

  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. 3642–3647

  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. 17–34

  16. Gärdenfors P, Warglien M (2012) Using Conceptual Spaces to Model Actions and Events. Journal of Semantics 29(4):487–519

    Article  MATH  Google Scholar 

  17. Gibson JJ (1979) The ecological approach to visual perception. Houghton Mifflin, Boston

    Google Scholar 

  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. Group AR Atlas transformation language. Online at http://www.eclipse.org/atl/

  20. Hartanto R (ed) (2011) A Hybrid Deliberative Layer for Robotic Agents: Fusing DL Reasoning with HTN Planning in Autonomous Robots. Springer-Verlag, Berlin, Heidelberg

    Google Scholar 

  21. Hartson HR (2003) Cognitive, physical, sensory, and functional affordances in interaction design. Behaviour & IT 22(5):315–338

    Google Scholar 

  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)

  23. Höller D (2013) Affordance-based action abstraction in robot planning. Master’s thesis, Bonn-Rhein-Sieg University of Applied Sciences

  24. Holt JC (1964) How Children Fail. Pitman

  25. Hubel N, Mohanarajah G, van de Molengraft R, Waibel M, D’Andrea R (2010) RoboEarth Project. Online at http://www.RoboEarth.org

  26. Hutchins E (1995) Cognition in the wild. MIT Press, Cambridge, MA

    Google Scholar 

  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 Maryland

  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 133–151

    Chapter  Google Scholar 

  29. Koppula HS, Saxena A (2013) Anticipating human activities using object affordances for reactive robotic response. In: Proceedings of Robotics: Science and Systems (RSS)

  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. 235–244

  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)

  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 433–440

  33. McKean E (ed) (2005) The New Oxford American Dictionary. Oxford University Press,

  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 30:1–44

    Article  Google Scholar 

  35. Moldovan B, Otterlo MV, Lopez PM, Santos-Victor J, Raedt LD (2011) Statistical relational learning of object affordances for robotic manipulation. In: ILP

  36. Nelson DGK (1999) Attention to functional properties in toddlers’ naming and problem-solving. Cognitive Development 14(1):77–100

    Article  Google Scholar 

  37. Norman D (2002) The psychology of everyday things. Basic Books, New York

    Google Scholar 

  38. Pandey AK (2012) Towards socially intelligent robots in human centered environment. Ph.D. thesis, University of Toulouse

  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)

  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):237–256

    Article  Google Scholar 

  41. Poggi I, D’Errico F (2011) Social signals: A psychological perspective. In: Computer Analysis of Human Behavior. Springer, pp. 185–225

  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)

  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 153–164

  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 91–105

    Chapter  Google Scholar 

  45. Reno RR, Cialdini RB, Kallgren CA (1993) The transsituational influence of social norms. Journal of Personality and Social Psychology 64

  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. 5047–5054

  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)

  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 II

  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 Communication

  50. Russell S, Norvig P (2003) Artificial Intelligence: A Modern Approach, 2nd edn. Prentice Hall,

  51. Sapir E (1921) Language: An introduction to the study of speech. Harcourt, Brace and company, New York

    Google Scholar 

  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, Germany

  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)

  54. Shpieva E, Awaad I (2013) Integrating the planning, execution and monitoring systems for a domestic service robot. In: Workshop on Roboterkontrollarchitekturen at Informatik

  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)

  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 435–444

  57. Steedman M (2002) Plans, affordances, and combinatory grammar. Linguistics and Philosophy 25

  58. Sun J (2008) Object categorization for affordance prediction. Ph.D. thesis, Georgia Institute of Technology

  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. 4261–4266

  60. The Eclipse Foundation: Eclipse Modeling Framework Project Core. Online at http://www.eclipse.org/modeling/emf/?project=emf (2013)

  61. Ugur E, Sahin E, Oztop E (2009) Predicting future object states using learned affordances. In: ISCIS, pp. 415–419

  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. 4312–4317

  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. 21–27

  64. Zhang J, Patel VL (2006) Distributed cognition, representation, and affordance. Cognition and Pragmatics 14(2):333–341

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Iman Awaad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Awaad, I., Kraetzschmar, G.K. & Hertzberg, J. The Role of Functional Affordances in Socializing Robots. Int J of Soc Robotics 7, 421–438 (2015). https://doi.org/10.1007/s12369-015-0281-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12369-015-0281-3

Keywords

Navigation