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CoBT: Collaborative Programming of Behaviour Trees from One Demonstration for Robot Manipulation (2404.05870v2)

Published 8 Apr 2024 in cs.RO

Abstract: Mass customization and shorter manufacturing cycles are becoming more important among small and medium-sized companies. However, classical industrial robots struggle to cope with product variation and dynamic environments. In this paper, we present CoBT, a collaborative programming by demonstration framework for generating reactive and modular behavior trees. CoBT relies on a single demonstration and a combination of data-driven machine learning methods with logic-based declarative learning to learn a task, thus eliminating the need for programming expertise or long development times. The proposed framework is experimentally validated on 7 manipulation tasks and we show that CoBT achieves approx. 93% success rate overall with an average of 7.5s programming time. We conduct a pilot study with non-expert users to provide feedback regarding the usability of CoBT.

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References (27)
  1. A. Jain, S. Mehak, P. Long, J. D. Kelleher, M. Guilfoyle, and M. C. Leva, “Evaluating safety and productivity relationship in human-robot collaboration,” Health, vol. 2022, pp. 08–28, 2022.
  2. J. E. Laird, K. Gluck, J. Anderson, K. D. Forbus, O. C. Jenkins, C. Lebiere, D. Salvucci, M. Scheutz, A. Thomaz, G. Trafton et al., “Interactive task learning,” IEEE Intelligent Systems, vol. 32, no. 4, pp. 6–21, 2017.
  3. M. Kyrarini, M. A. Haseeb, D. Ristić-Durrant, and A. Gräser, “Robot learning of industrial assembly task via human demonstrations,” Autonomous Robots, vol. 43, pp. 239–257, 2019.
  4. G. Lentini, G. Grioli, M. G. Catalano, and A. Bicchi, “Robot programming without coding,” in 2020 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2020, pp. 7576–7582.
  5. M. Knaust and D. Koert, “Guided robot skill learning: A user-study on learning probabilistic movement primitives with non-experts,” in 2020 IEEE-RAS 20th International Conference on Humanoid Robots (Humanoids).   IEEE, 2021, pp. 514–521.
  6. O. Gustavsson, M. Iovino, J. Styrud, and C. Smith, “Combining context awareness and planning to learn behavior trees from demonstration,” in 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).   IEEE, 2022, pp. 1153–1160.
  7. G. Suddrey, B. Talbot, and F. Maire, “Learning and executing re-usable behaviour trees from natural language instruction,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 10 643–10 650, 2022.
  8. T. Eiband, C. Willibald, I. Tannert, B. Weber, and D. Lee, “Collaborative programming of robotic task decisions and recovery behaviors,” Autonomous Robots, vol. 47, no. 2, pp. 229–247, 2023.
  9. O. Kroemer, C. Daniel, G. Neumann, H. Van Hoof, and J. Peters, “Towards learning hierarchical skills for multi-phase manipulation tasks,” in 2015 IEEE international conference on robotics and automation (ICRA).   IEEE, 2015, pp. 1503–1510.
  10. T. Osa, J. Pajarinen, G. Neumann, J. A. Bagnell, P. Abbeel, J. Peters et al., “An algorithmic perspective on imitation learning,” Foundations and Trends® in Robotics, vol. 7, no. 1-2, pp. 1–179, 2018.
  11. M. Diehl, C. Paxton, and K. Ramirez-Amaro, “Automated generation of robotic planning domains from observations,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 6732–6738.
  12. L. Scherf, A. Schmidt, S. Pal, and D. Koert, “Interactively learning behavior trees from imperfect human demonstrations,” Frontiers in Robotics and AI, vol. 10, 2023.
  13. A. M. Zanchettin, “Symbolic representation of what robots are taught in one demonstration,” Robotics and Autonomous Systems, vol. 166, p. 104452, 2023.
  14. Y. S. Liang, D. Pellier, H. Fiorino, and S. Pesty, “iropro: An interactive robot programming framework,” International Journal of Social Robotics, pp. 1–15, 2022.
  15. K. French, S. Wu, T. Pan, Z. Zhou, and O. C. Jenkins, “Learning behavior trees from demonstration,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 7791–7797.
  16. C. Truong, L. Oudre, and N. Vayatis, “Selective review of offline change point detection methods,” Signal Processing, vol. 167, p. 107299, 2020.
  17. A. Ude, B. Nemec, T. Petrić, and J. Morimoto, “Orientation in cartesian space dynamic movement primitives,” in 2014 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2014, pp. 2997–3004.
  18. N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford et al., “The limits and potentials of deep learning for robotics,” The International journal of robotics research, vol. 37, no. 4-5, pp. 405–420, 2018.
  19. P. Kordjamshidi, D. Roth, and K. Kersting, “Declarative learning-based programming as an interface to ai systems,” Frontiers in Artificial Intelligence, vol. 5, 2022.
  20. D. H. Grollman and O. C. Jenkins, “Incremental learning of subtasks from unsegmented demonstration,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.   IEEE, 2010, pp. 261–266.
  21. N. Nejati, P. Langley, and T. Konik, “Learning hierarchical task networks by observation,” in Proceedings of the 23rd international conference on Machine learning, 2006, pp. 665–672.
  22. M. Colledanchise, D. Almeida, and P. Ögren, “Towards blended reactive planning and acting using behavior trees,” in 2019 International Conference on Robotics and Automation (ICRA).   IEEE, 2019, pp. 8839–8845.
  23. E. Valassakis, G. Papagiannis, N. Di Palo, and E. Johns, “Demonstrate once, imitate immediately (dome): Learning visual servoing for one-shot imitation learning,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2022, pp. 8614–8621.
  24. S. Jiménez, T. De La Rosa, S. Fernández, F. Fernández, and D. Borrajo, “A review of machine learning for automated planning,” The Knowledge Engineering Review, vol. 27, no. 4, pp. 433–467, 2012.
  25. S. G. Hart and L. E. Staveland, “Development of nasa-tlx (task load index): Results of empirical and theoretical research,” in Advances in psychology.   Elsevier, 1988, vol. 52, pp. 139–183.
  26. J. Brooke, “Sus: a “quick and dirty’usability,” Usability evaluation in industry, vol. 189, no. 3, pp. 189–194, 1996.
  27. M. Iovino, F. I. Doğan, I. Leite, and C. Smith, “Interactive disambiguation for behavior tree execution,” in 2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).   IEEE, 2022, pp. 82–89.
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Authors (5)
  1. Aayush Jain (10 papers)
  2. Philip Long (8 papers)
  3. Valeria Villani (12 papers)
  4. John D. Kelleher (37 papers)
  5. Maria Chiara Leva (3 papers)

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