Skip to main content
SpringerLink
Log in

Experience-Based Planning Domains: an Integrated Learning and Deliberation Approach for Intelligent Robots

Robot Task Learning from Human Instructions

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

Deliberation and learning are required to endow a robot with the capabilities for acquiring knowledge, performing a variety of tasks and interactions, and adapting to open-ended environments. This paper presents the notion of experience-based planning domains (EBPDs) for task level learning and planning in robotics. EBPDs provide methods for a robot to: (i) obtain robot activity experiences from the robot’s performance in a dynamic environment; (ii) conceptualize each experience producing an activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction, goal inference and feature extraction. A high-level task planner was developed to find a solution for a task by following an activity schema. The proposed approach is illustrated and evaluated in a restaurant environment where a service robot learns how to carry out complex tasks.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Agostini, A.G., Torras, C., Wörgötter, F.: Integrating task planning and interactive learning for robots to work in human environments. In: International Joint Conference on Artificial Intelligence, pp. 2386–2391. AAAI Press. Association for the Advancement of Artificial Intelligence (2011)

  2. Argall, B.D., Chernova, S., Veloso, M., Browning, B.: A survey of robot learning from demonstration. Robot. Auton. Syst. 57(5), 469–483 (2009)

    Article  Google Scholar 

  3. Arnaboldi, V., Conti, M., Passarella, A., Pezzoni, F.: Analysis of ego network structure in online social networks. In: Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (Socialcom), pp. 31–40 (2012)

  4. Bergmann, R., Wilke, W.: Building and refining abstract planning cases by change of representation language. J. Artif. Intell. Res. 3, 53–118 (1995)

    Google Scholar 

  5. Chauhan, A., Seabra Lopes, L., Tomé, A.M., Pinho, A.: Towards supervised acquisition of robot activity experiences: an ontology-based approach. In: 16Th Portuguese Conference on Artificial Intelligence - EPIA’2013 (2013)

  6. Chrpa, L.: Generation of macro-operators via investigation of action dependencies in plans. Knowl. Eng. Rev. 25(03), 281–297 (2010)

    Article  Google Scholar 

  7. Connell, J.H., Mahadevan, S.: Robot Learning, the Springer International Series in Engineering and Computer Science, vol. 233. Springer (1993)

  8. Ekvall, S., Kragic, D.: Robot learning from demonstration: a task-level planning approach. Int. J. Adv. Robot. Syst. 5(3) (2008)

  9. Fikes, R.E., Hart, P.E., Nilsson, N.J.: Learning and executing generalized robot plans. Artif. Intell. 3, 251–288 (1972)

    Article  Google Scholar 

  10. Hertzberg, J., Zhang, J., Zhang, L., Rockel, S., Neumann, B., Lehmann, J., Dubba, K., Cohn, A., Saffiotti, A., Pecora, F., Mansouri, M., Konec̆ný, S., Günther, M., Stock, S., Seabra Lopes, L., Oliveira, M., Lim, G., Kasaei, H., Mokhtari, V., Hotz, L., Bohlken, W.: The race project. KI - Künstliche Intelligenz 28(4), 297–304 (2014)

    Article  Google Scholar 

  11. Hogg, C., Muñoz-Avila, H., Kuter, U.: Learning hierarchical task models from input traces. Comput. Intell. 1, 3–48 (2016)

  12. Hu, Y., De Giacomo, G.: Generalized planning: synthesizing plans that work for multiple environments. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol. 22, pp. 918–923 (2011)

  13. Ingrand, F., Ghallab, M.: Deliberation for autonomous robots: a survey. Artif. Intell. (2015). doi:10.1016/j.artint.2014.11.003

  14. Jiménez, S., De La Rosa, T., Fernández, S., Fernández, F., Borrajo, D.: A review of machine learning for automated planning. Knowl. Eng. Rev. 27, 433–467 (2012)

    Article  Google Scholar 

  15. Konec̆ný, S̆., Stock, S., Pecora, F., Saffiotti, A.: Planning domain + execution semantics: a way towards robust execution?. In: Qualitative Representations for Robots, AAAI Spring Symposium (2014)

  16. Kuniyoshi, Y., Inaba, M., Inoue, H.: Learning by watching: extracting reusable task knowledge from visual observation of human performance. IEEE Trans. Robot. Autom. 10(6), 799–822 (1994)

    Article  Google Scholar 

  17. Mcdermott, D., Ghallab, M., Howe, A., Knoblock, C., Ram, A., Veloso, M., Weld, D., Wilkins, D.: PDDL - the Planning Domain Definition Language. Tech. Rep., CVC TR-98-003/DCS TR-1165, Yale Center for Computational Vision and Control (1998)

  18. Mitchell, T., Keller, R., Kedar-Cabelli, S.: Explanation-based generalization: a unifying view. Mach. Learn. 1(1), 47–80 (1986)

    Google Scholar 

  19. Mohseni-Kabir, A., Rich, C., Chernova, S., Sidner, C.L., Miller, D.: Interactive hierarchical task learning from a single demonstration. In: Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI ’15, pp 205–212. ACM, New York, NY, USA (2015)

    Google Scholar 

  20. Mokhtari, V., Lim, G., Seabra Lopes, L., Pinho, A.: Gathering and conceptualizing plan-based robot activity experiences. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds.) Intelligent Autonomous Systems 13, Advances in Intelligent Systems and Computing, vol. 302, pp. 993–1005. Springer (2016)

  21. Mokhtari, V., Seabra Lopes, L., Pinho, A.J., Lim, G.H.: Planning with activity schemata: closing the loop in experience-based planning. In: IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 2015, pp. 9–14 (2015)

  22. Nicolescu, M.N., Mataric, M.J.: Natural methods for robot task learning: instructive demonstrations, generalization and practice. In: Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 241–248. ACM (2003)

  23. Rockel, S., Neumann, B., Zhang, J., Dubba, K.S.R., Cohn, A.G., Konec̆ný, S̆., Mansouri, M., Pecora, F., Saffiotti, A., Günther, M., Stock, S., Hertzberg, J., Tomé, A.M., Pinho, A.J., Seabra Lopes, L., von Riegen, S., Hotz, L.: An ontology-based multi-level robot architecture for learning from experiences. In: Designing Intelligent Robots: Reintegrating AI II, AAAI Spring Symposium. Stanford (USA) (2013)

  24. Russell, S., Norvig, P.: Artificial Intelligence: a Modern Approach, 3rd edn. Prentice Hall (2010)

  25. Rybski, P., Yoon, K., Stolarz, J., Veloso, M.: Interactive robot task training through dialog and demonstration. In: 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2007, pp. 49–56 (2007)

  26. Seabra Lopes, L.: Robot Learning at the Task Level. A Study in the Assembly Domain. Ph.D. Thesis, Universidade Nova de Lisboa, Monte da Caparica (1997)

  27. Seabra Lopes, L.: Failure recovery planning in assembly based on acquired experience: learning by analogy. In: Proceedings of the IEEE International Symposium on Assembly and Task Planning (ISATP’99), pp. 294–300. IEEE (1999)

  28. Seabra Lopes, L.: Failure recovery planning for robotized assembly based on learned semantic structures. In: IFAC Workshop on Intelligent Assembly and Disassembly (IAD’2007), pp. 65–70 (2007)

  29. Seabra Lopes, L., Connell, J.: Semisentient robots: routes to integrated intelligence. IEEE Intell. Syst. 16(5), 10–14 (2001)

    Google Scholar 

  30. She, L., Yang, S., Cheng, Y., Jia, Y., Chai, J.Y., Xi, N.: Back to the blocks world: learning new actions through situated human-robot dialogue. In: 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 89 (2014)

  31. Veeraraghavan, H., Veloso, M.: Teaching sequential tasks with repetition through demonstration. In: Proceedings of the 7Th International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3, AAMAS ’08, pp. 1357–1360. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC (2008)

  32. Wood, R., Baxter, P., Belpaeme, T.: A review of long-term memory in natural and synthetic systems. Adapt. Behav. 20(2), 81–103 (2012)

    Article  Google Scholar 

  33. Zimmerman, T., Kambhampati, S.: Learning-assisted automated planning: looking back, taking stock, going forward. AI Mag. 24(2), 73–96 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IEETA - Institute of Electronics and Telematics Engineering, University of Aveiro, Aveiro, Portugal

    Vahid Mokhtari, Luís Seabra Lopes & Armando J. Pinho

  2. DETI - Department of Electronics, Telecommunications and Informatics, University of Aveiro, Aveiro, Portugal

    Luís Seabra Lopes & Armando J. Pinho

Authors
  1. Vahid Mokhtari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luís Seabra Lopes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Armando J. Pinho
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vahid Mokhtari.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mokhtari, V., Seabra Lopes, L. & Pinho, A.J. Experience-Based Planning Domains: an Integrated Learning and Deliberation Approach for Intelligent Robots. J Intell Robot Syst 83, 463–483 (2016). https://doi.org/10.1007/s10846-016-0371-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-016-0371-y

Keywords

Search

Navigation