Skip to main content
Log in

Biasing behavioral activation with intent for an entertainment robot

  • Original Research Paper
  • Published:
Intelligent Service Robotics Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Deliberate control of an entertainment robot presents a special problem in balancing the requirement for intentional behavior with the existing mechanisms for autonomous action selection. It is proposed that the intentional biasing of activation in lower-level reactive behaviors is the proper mechanism for realizing such deliberative action. In addition, it is suggested that directed intentional bias can result in goal-oriented behavior without subsuming the underlying action selection used to generate natural behavior. This objective is realized through a structure called the intentional bus. The intentional bus serves as the interface between deliberative and reactive control by realizing high-level goals through the modulation of intentional signals sent to the reactive layer. A deliberative architecture that uses the intentional bus to realize planned behavior is described. In addition, it is shown how the intentional bus framework can be expanded to support the serialization of planned behavior by shifting from direct intentional influence for plan execution to attentional triggering of a learned action sequence. Finally, an implementation of this architecture, developed and tested on Sony’s humanoid robot QRIO, is described.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Andronache V, Scheutz M (2002) Contention scheduling: a viable action-selection mechanism of robotics? In: Proceedings of the 13th midwest AI and cognitive science conference. AAAI, New York, pp 122–129

  2. Arkin R (1998) Behavior-based robotics. MIT Press, Cambridge

    Google Scholar 

  3. Arkin R, Clark R, Ram A (1992) Learning momentum: on-line performance enhancement for reactive systems. In: Proceedings of the 1992 IEEE international conference on robotics and automation, pp 111–116

  4. Arkin R, Fujita M, Takagi T, Hasegawa R (2003) An ethological basis for human–robot interaction. Robot Autonomous Syst 42(3–4): 191–201

    Article  MATH  Google Scholar 

  5. Blumberg B (1994) Action-selection in hamsterdam: lessons in ethology. In: Proceedings of the third international conference on the simulation of adaptive behavior, pp 108–117

  6. Bonasso R, Kortenkamp D (1994) An intelligent agent architecture in which to pursue robot learning. In: Working notes: MCL-COLT ’94 robot learning workshop

  7. Brazeal C (2000) Sociable machines: expresive social exchange between humans and robots. Ph.D. thesis, MIT Press, Cambridge

  8. Burgard W, Cremers A, Fox D, Hähnel D, Lakemeyer G, Schulz D, Steiner V, Thrun S (1998) Interactive museum tour-guide robot. In: Proceedings of AAAI-98, pp 11–18

  9. Carpenter P, Riley P, Veloso M, Kaminka G (2002) Integration of advice in an action selection architecture. In: Proceedings of the RoboCup2002 symposium, pp 195–205

  10. Cheranova S, Arkin R (2007) From deliberative to routine behaviors: A cognitively-inspired action selection mechanism for routine behavior capture. Adapt Behav 15(2): 199–216

    Article  Google Scholar 

  11. Cooper R, Shallice T (1997) Modeling the selection of routine action: Exploring the criticality of parameter values. In: Proceedings of the 19th annual conference of the cognitive science society, pp 130–135

  12. Cooper R, Shallice T (2000) Contention scheduling and the control of routine activities. Cognit Neuropsychol 17(4): 297–338

    Article  Google Scholar 

  13. Fujita M, Kuroki Y, Ishida T, Doi T (2003) Autonomous behavior control architecture of entertainment humanoid robot SDR-4X. In: Proceedings of the IEEE/RSJ international conference on intelligent robots and systems, pp 960–967

  14. Garforth J, Meehan A (2005) Driven by novelty? Integrating executive attention and emotion for autonomous cognitive development. In: Proceedings of the developmental robotics AAAI spring symposium

  15. Garforth J, Meehan A, Mc Hale S (2000) Attentional behavior in robotic agents. In: Proceedings of the international workshop on recent advances in mobile robots

  16. Gat E (1997) Artificial intelligence and mobile robots. MIT/AAAI Press, Cambridge

    Google Scholar 

  17. Glasspool D (2000) The integration of control and behavior: Insights from neuroscience and AI. In: Proceedings of the how to design a functioning mind symposium at AISB-2000, pp 77–84

  18. Hoshino Y, Takagi T, Profio UD, Fujita M (2004) Behavior description and control using behavior module for personal robot. In: Proceedings of the 2004 international conference on robotics and automation, pp 4165–4171

  19. Lyons D, Hendricks A (1992) Planning for reactive robot behavior. In: Proceedings of the IEEE international conference on robotics and automation, pp 2675–2680

  20. Maes P (1989) How to do the right thing. Connect Sci J 1(3): 291–323

    Article  Google Scholar 

  21. Mastrogiovanni F, Sgorbissa A, Zaccari R (2004) A system for hierarchical planning in service mobile robots. In: Proceedings of IAS08

  22. Moshinka L, Arkin R (2003) On TAMEing robots. In: Proceedings IEEE conference on systems, man, and cybernetics, pp 3949–3959

  23. Norman DA, Shallice T (1986) Consciousness and self regulation: advances in theory and research. Attention to action: willed and automatic control of behavior, vol 4. Academic Press, New York

    Google Scholar 

  24. Okada K, Haneda A, Nakai H, Inaba M, Inoue H (2004) Environment manipulation planner for humanoid robots using task graph that generates action sequence. In: Proceedings of 2004 international conference on intelligent robots and systems, pp 1174–1179

  25. Payton D, Rosenblatt J, Keirsey D (1990) Plan guided reaction. IEEE Trans Syst Man Cybernet 20(6): 1370–1382

    Article  Google Scholar 

  26. Rosenblatt J (1997) DAMN: A distributed architecture for mobile navigation. J Exp Theor Artif Intell 9(2): 339–360

    Article  Google Scholar 

  27. Schallice T, Burgess P (1996) The domain of supervisory processes and temporal organization of behavior. Philos TransRoy Soc London B 351: 1405–1412

    Article  Google Scholar 

  28. Wagner A, Arkin R (2003) Internalized plans for communication-sensitive team behaviors. In: Proceedings of the international conference on intelligent robotics and systems, pp 2480–2487

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Patrick D. Ulam.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ulam, P.D., Arkin, R.C. Biasing behavioral activation with intent for an entertainment robot. Intel Serv Robotics 1, 195–209 (2008). https://doi.org/10.1007/s11370-008-0021-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11370-008-0021-8

Keywords