Natural Language Interpretation for an Interactive Service Robot in Domestic Domains

  • Stefan Schiffer
  • Niklas Hoppe
  • Gerhard Lakemeyer
Part of the Communications in Computer and Information Science book series (CCIS, volume 358)

Abstract

In this paper, we propose a flexible system for robust natural language interpretation of spoken commands on a mobile robot in domestic service robotics applications. Existing language processing for instructing a mobile robot is often restricted by using a simple grammar where precisely pre-defined utterances are directly mapped to system calls. These approaches do not regard fallibility of human users and they only allow for binary processing of an utterance; either a command is part of the grammar and hence understood correctly, or it is not part of the grammar and gets rejected. We model the language processing as an interpretation process where the utterance needs to be mapped to the robot’s capabilities. We do so by casting the processing as a (decision-theoretic) planning problem on interpretation actions. This allows for a flexible system that can resolve ambiguities and which is also capable of initiating steps to achieve clarification. We show how we evaluated several versions of the system with multiple utterances of different complexity as well as with incomplete and erroneous requests.

Keywords

Natural Language Processing Interpretation Decision-theoretic Planning Domestic Service Robotics RoboCup@Home 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Austin, J.L.: How to Do Things with Words, 2 edn. Harvard University Press (1975)Google Scholar
  2. 2.
    Beetz, M., Arbuckle, T., Belker, T., Cremers, A.B., Schulz, D.: Integrated plan-based control of autonomous robots in human environments. IEEE Intelligent Systems 16(5), 56–65 (2001)CrossRefGoogle Scholar
  3. 3.
    Boutilier, C., Reiter, R., Soutchanski, M., Thrun, S.: Decision-theoretic, high-level agent programming in the situation calculus. In: Proc. of the 17th Nat’l Conf. on Artificial Intelligence (AAAI 2000), pp. 355–362. AAAI Press/The MIT Press (2000)Google Scholar
  4. 4.
    Clodic, A., Alami, R., Montreuil, V., Li, S., Wrede, B., Swadzba, A.: A study of interaction between dialog and decision for human-robot collaborative task achievement. In: Proc. of the International Symposium on Robot and Human Interactive Communication (RO-MAN 2007), August 26-29, pp. 913–918 (2007)Google Scholar
  5. 5.
    Cohen, P.R., Levesque, H.J.: Speech acts and rationality. In: Proc. of the 23rd Annual Meeting on Association for Computational Linguistics, pp. 49–60 (1985)Google Scholar
  6. 6.
    Cornish, D., Dukette, D.: The Essential 20: Twenty Components of an Excellent Health Care Team. RoseDog Books (2009)Google Scholar
  7. 7.
    Doostdar, M., Schiffer, S., Lakemeyer, G.: A Robust Speech Recognition System for Service-Robotics Applications. In: Iocchi, L., Matsubara, H., Weitzenfeld, A., Zhou, C. (eds.) RoboCup 2008. LNCS (LNAI), vol. 5399, pp. 1–12. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. 8.
    Ervin-Tripp, S.: Is Sybil there? The structure of some American English directives. Language in Society 5(01), 25–66 (1976)CrossRefGoogle Scholar
  9. 9.
    Ferrein, A., Lakemeyer, G.: Logic-based robot control in highly dynamic domains. Robotics and Autonomous Systems 56(11), 980–991 (2008), Special Issue on Semantic Knowledge in RoboticsCrossRefGoogle Scholar
  10. 10.
    Fong, T., Thorpe, C., Baur, C.: Collaboration, dialogue, human-robot interaction. In: Robotics Research. Springer Tracts in Advanced Robotics, vol. 6, pp. 255–266. Springer (2003)Google Scholar
  11. 11.
    Görz, G., Ludwig, B.: Speech Dialogue Systems - A Pragmatics-Guided Approach to Rational Interaction. KI–Künstliche Intelligenz 10(3), 5–10 (2005)Google Scholar
  12. 12.
    Gu, Y., Soutchanski, M.: Reasoning about large taxonomies of actions. In: Proc. of the 23rd Nat’l Conf. on Artificial Intelligence, pp. 931–937. AAAI Press (2008)Google Scholar
  13. 13.
    Levesque, H.J., Reiter, R., Lespérance, Y., Lin, F., Scherl, R.B.: Golog: A logic programming language for dynamic domains. J. Logic Program 31(1-3), 59–84 (1997)MATHCrossRefGoogle Scholar
  14. 14.
    McCarthy, J.: Situations, Actions, and Causal Laws. Technical Report Memo 2, AI Lab, Stanford University, California, USA (July 3, 1963)Google Scholar
  15. 15.
    Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc. (1994)Google Scholar
  16. 16.
    Reiter, R.: Knowledge in Action. Logical Foundations for Specifying and Implementing Dynamical Systems. MIT Press (2001)Google Scholar
  17. 17.
    Schiffer, S., Hoppe, N., Lakemeyer, G.: Flexible command interpretation on an interactive domestic service robot. In: Proc. of the 4th International Conference on Agents and Artificial Intelligence (ICAART), pp. 26–35. SciTePress (2012)Google Scholar
  18. 18.
    Scowen, R.: Extended BNF – generic base standards. In: Proc. of the Software Engineering Standards Symposium, August 30-September 03, pp. 25–34 (1993)Google Scholar
  19. 19.
    Searle, J.R.: Speech Acts: An Essay in the Philosophy of Language. Cambridge University Press, Cambridge (1969)Google Scholar
  20. 20.
    Shieber, S.: Evidence against the context-freeness of natural language. Linguistics and Philosophy 8(3), 333–343 (1985)CrossRefGoogle Scholar
  21. 21.
    Wisspeintner, T., van der Zant, T., Iocchi, L., Schiffer, S.: Robocup@home: Scientific Competition and Benchmarking for Domestic Service Robots. Interaction Studies. Special Issue on Robots in the Wild 10(3), 392–426 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Stefan Schiffer
    • 1
  • Niklas Hoppe
    • 1
  • Gerhard Lakemeyer
    • 1
  1. 1.Knowledge-Based Systems GroupRWTH Aachen UniversityAachenGermany

Personalised recommendations