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State-Aware Layered BTs—Behavior Tree Extensions for Post-Actions, Preferences and Local Priorities in Robotic Applications

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Intelligent Autonomous Systems 17 (IAS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 577))

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Abstract

In this paper we propose State-Aware Layered BTs, a behavior tree extension that facilitates the implementation of post-actions, preferences, and local priorities for usage in robotics applications. These procedures are hard to be defined on standard behavior tree formulations because behavior trees lack the ability to structurally retain any information on previous states. Therefore, the execution of localized heuristics can only be attained through the inference or emulation of previous states, which can be imprecise, cause reactivity losses, or introduce added complexity to the structure. In this work we cope with this problem by (i) adding a native operator which accesses the previous execution states of individual nodes; (ii) adding separate layers of interaction which expand the operator functionality and are used to describe multiple goals within the same task. The validity of the proposed system is verified through extensive analysis of a series of annotated examples.

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References

  1. Behavior Trees | Unreal Engine Documentation (2022). https://docs.unrealengine.com/4.27/en-US/InteractiveExperiences/ArtificialIntelligence/BehaviorTrees/

  2. Behavior trees for AI: How they work (2022). https://www.gamedeveloper.com/programming/behavior-trees-for-ai-how-they-work

  3. BehaviorTree.CPP (2022). https://github.com/BehaviorTree/BehaviorTree.CPP. Retrieved 2022

  4. Andrew Bagnell, J., Cavalcanti, F., Cui, L., Galluzzo, T., Hebert, M., Kazemi, M., Klingensmith, M., Libby, J., Liu, T.Y., Pollard, N., et al.: An integrated system for autonomous robotics manipulation. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2955–2962, IEEE (2012)

    Google Scholar 

  5. Brooks, R.A.: Achieving Artificial Intelligence Through Building Robots. Technical report, Massachusetts Inst of Tech Cambridge Artificial Intelligence Lab (1986)

    Google Scholar 

  6. Champandard, A.J., Dunstan, P.: The behavior tree starter kit. In Game AI Pro, pp. 27–46. CRC Press (2013)

    Google Scholar 

  7. Colledanchise, M., Almeida, D., Ögren, P.: Towards blended reactive planning and acting using behavior trees. In: 2019 International Conference on Robotics and Automation (ICRA), pp. 8839–8845, IEEE (2019)

    Google Scholar 

  8. Colledanchise, M., Ögren, P.: How behavior trees modularize hybrid control systems and generalize sequential behavior compositions, the subsumption architecture, and decision trees. IEEE Trans. Robot. 33(2), 372–389 (2016)

    Article  Google Scholar 

  9. Colledanchise, M., Ögren, P.: Behavior Trees in Robotics and AI: An Introduction. CRC Press (2018)

    Google Scholar 

  10. Colvin, R., Grunske, L., Winter, K.: Probabilistic timed behavior trees. In: International Conference on Integrated Formal Methods, pp 156–175. Springer, Heidelberg (2007)

    Google Scholar 

  11. Flórez-Puga, G., Gomez-Martin, M.A., Gomez-Martin, P.P., Díaz-Agudo, B., González-Calero, P.A.: Query-enabled behavior trees. IEEE Trans. Comput. Intell. AI Games 1(4), 298–308 (2009)

    Google Scholar 

  12. Ghzouli, R., Berger, T., Johnsen, E.B., Dragule, S., Wasowski, A.: Behavior trees in action: a study of robotics applications. In: Proceedings of the 13th ACM SIGPLAN International Conference on Software Language Engineering, pp. 196–209 (2020)

    Google Scholar 

  13. Giunchiglia, E., Colledanchise, M., Natale, L., Tacchella, A.: Conditional behavior trees: definition, executability, and applications. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 1899–1906, IEEE (2019)

    Google Scholar 

  14. Iovino, M., Scukins, E., Styrud, J., Ögren, P., Smith, C.: A survey of behavior trees in robotics and AI. arXiv preprint arXiv:2005.05842 (2020)

  15. Isla, D.: GDC 2005 Proceeding: Handling Complexity in the HALO 2 AI. Retrieved October, 21:2009 (2005)

    Google Scholar 

  16. Klöckner, A.: Behavior trees with stateful tasks. In: Advances in Aerospace Guidance, Navigation and Control, pp. 509–519. Springer, Heidelberg (2015)

    Google Scholar 

  17. Lim, C.-U., Baumgarten, R., Colton, S.: Evolving behaviour trees for the commercial game defcon. In: European Conference on the Applications of Evolutionary Computation, pp. 100–110. Springer, Heidelberg (2010)

    Google Scholar 

  18. Marzinotto, A., Colledanchise, M., Smith, C., Ögren, P.: Towards a unified behavior trees framework for robot control. In: 2014 IEEE International Conference on Robotics and Automation (ICRA), pp. 5420–5427, IEEE (2014)

    Google Scholar 

  19. Mateas, M., Stern, A.: A behavior language for story-based believable agents. IEEE Intell. Syst. 17(4), 39–47 (2002)

    Article  Google Scholar 

  20. Merrill, B.: Building utility decisions into your existing behavior tree. In: Game AI Pro, 127 (2013)

    Google Scholar 

  21. Ocio, S.: Adapting AI behaviors to players in driver San Francisco: hinted-execution behavior trees. In: Eighth Artificial Intelligence and Interactive Digital Entertainment Conference (2012)

    Google Scholar 

  22. Ogren, P.: Increasing modularity of UAV control systems using computer game behavior trees. In: AIAA Guidance, Navigation, and Control Conference, p. 4458 (2012)

    Google Scholar 

  23. Kenneth Rosenblatt, J., Payton, D.: A fine-grained alternative to the subsumption architecture for mobile robot control. In: Proceedings of the IEEE/INNS International Joint Conference on Neural Networks, vol. 2, pp. 317–324 (1989)

    Google Scholar 

  24. Rovida, F., Grossmann, B., Krüger, V.: Extended behavior trees for quick definition of flexible robotic tasks. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6793–6800, IEEE (2017)

    Google Scholar 

  25. Rovida, F., Wuthier, D., Grossmann, B., Fumagalli, M., Krüger, V.: Motion generators combined with behavior trees: a novel approach to skill modelling. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5964–5971, IEEE (2018)

    Google Scholar 

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Author information

Authors and Affiliations

  1. The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan

    Guilherme de Campos Affonso, Kei Okada & Masayuki Inaba

Authors
  1. Guilherme de Campos Affonso
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  2. Kei Okada
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  3. Masayuki Inaba
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Corresponding author

Correspondence to Guilherme de Campos Affonso .

Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, University of Zagreb, Zagreb, Croatia

    Ivan Petrovic

  2. Department of Information Engineering, University of Padua, Padua, Italy

    Emanuele Menegatti

  3. Faculty of Electrical Engineering, University of Zagreb, Zagreb, Croatia

    Ivan Marković

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de Campos Affonso, G., Okada, K., Inaba, M. (2023). State-Aware Layered BTs—Behavior Tree Extensions for Post-Actions, Preferences and Local Priorities in Robotic Applications. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_45

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