Skip to main content
SpringerLink
Log in

Advances in Robot Programming by Demonstration

  • Fachbeitrag
  • Published:
KI - Künstliche Intelligenz Aims and scope Submit manuscript

Abstract

Robot Programming by Demonstration (PbD) has been dealt with in the literature as a promising way to teach robots new skills in an intuitive way. In this paper we describe our current work in the field toward the implementation of PbD system which allows robots to learn continuously from human observation, build generalized representations of human demonstration and apply such representations to new situations.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Schaal S (1999) Is imitation learning the route to humanoid robots? Trends Cogn Sci 3(6):233–242

    Article  Google Scholar 

  2. Dillmann R (2004) Teaching and learning of robot tasks via observation of human performance. Robot Auton Syst 47(2–3):109–116

    Article  Google Scholar 

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

    Article  Google Scholar 

  4. Breazeal C, Scassellati B (2002) Robots that imitate humans. Trends Cogn Sci 6(11):481–487

    Article  Google Scholar 

  5. Schaal S, Ijspeert A, Billard A (2003) Computational approaches to motor learning by imitation. Philos Trans R Soc Lond B, Biol Sci 358(1431):537–547

    Article  Google Scholar 

  6. Billard A, Siegwart R (2004) Robot learning from demonstration. Robot Auton Syst 47(2–3):65–67

    Article  Google Scholar 

  7. Billard A, Calinon S, Dillmann R, Schaal S (2008) Robot programming by demonstration. In: Siciliano B, Khatib O (eds) Handbook of robotics. Springer, Secaucus, pp 1371–1394

    Chapter  Google Scholar 

  8. Ude A (1993) Trajectory generation from noisy positions of object features for teaching robot paths. Robot Auton Syst 11(2):113–127

    Article  Google Scholar 

  9. Yang J, Xu Y, Chen CS (1997) Human action learning via hidden Markov model. IEEE Trans Syst Man Cybern, Part A, Syst Hum 27(1):34–44

    Article  Google Scholar 

  10. Yamane K, Nakamura Y (2003) Dynamics filter—concept and implementation of online motion generator for human figures. IEEE Trans Robot Autom 19(3):421–432

    Article  Google Scholar 

  11. Ijspeert A, Nakanishi J, Schaal S (2002) Movement imitation with nonlinear dynamical systems in humanoid robots. In: IEEE international conference on robotics and automation, ICRA2002, pp 1398–1403

  12. Ude A, Atkeson CG, Riley M (2004) Programming full-body movements for humanoid robots by observation. Robot Auton Syst 47:93–108

    Article  Google Scholar 

  13. Asfour T, Azad P, Gyarfas F, Dillmann R (2008) Imitation learning of dual-arm manipulation tasks in humanoid robots. Int J Humanoid Robot 5(2):183–202

    Article  Google Scholar 

  14. Calinon S, Guenter F, Billard A (2007) On learning, representing and generalizing a task in a humanoid robot. IEEE Trans Syst Man Cybern, Part B 37(2):286–298. Special issue on robot learning by observation, demonstration and imitation

    Article  Google Scholar 

  15. Muench S, Kreuziger J, Kaiser M, Dillmann R (1994) Robot programming be demonstration (RPD)—using machine learning and user interaction methods for the development of easy and comfortable robot programming systems. In: Proceedings of international symposium on industrial robots, ISIR, pp 685–693

  16. Friedrich H, Münch S, Dillmann R, Bocionek S, Sassin M (1996) Robot programming by demonstration (RPD): supporting the induction by human interaction. Mach Learn 23(2–3):163–189

    Google Scholar 

  17. Nicolescu M, Mataric M (2003) 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, AAMAS, New York, NY, USA. ACM, New York, pp 241–248

    Chapter  Google Scholar 

  18. Ekvall S, Kragic D (2006) Learning task models from multiple human demonstrations. In: IEEE international symposium on robot and human interactive communication, ROMAN, pp 358–363

  19. Pardowitz M, Knoop S, Dillmann R, Zollner R (2007) Incremental learning of tasks from user demonstrations, past experiences, and vocal comments. IEEE Trans Syst Man Cybern, Part B, Cybern 37(2):322–332

    Article  Google Scholar 

  20. Alissandrakis A, Nehaniv CL, Dautenhahn K (2007) Correspondence mapping induced state and action metrics for robotic imitation. IEEE Trans Syst Man Cybern, Part B, Cybern 37(2):299–307

    Article  Google Scholar 

  21. Inamura T, Toshima I, Tanie H, Nakamura Y (2004) Embodied symbol emergence based on mimesis theory. Int J Robot Res 23(4–5):363–377

    Article  Google Scholar 

  22. Jenkins OC, Mataric MJ (2004) Performance-derived behavior vocabularies: data-driven acquisition of skills from motion. Int J Humanoid Robot 1(2):237–288

    Article  Google Scholar 

  23. Jenkins OC, Matarić MJ (2004) A spatio-temporal extension to Isomap nonlinear dimension reduction. In: Proceedings of the twenty-first international conference on machine learning, ICML, New York, NY, USA. ACM, New York, p 56

    Chapter  Google Scholar 

  24. Lee D, Nakamura Y (2006) Stochastic model of imitating a new observed motion based on the acquired motion primitives. In: IROS, pp 4994–5000

  25. Kadone H, Nakamura Y (2006) Segmentation, memorization, recognition and abstraction of humanoid motions based on correlations and associative memory. In: 6th IEEE-RAS international conference on Humanoid robots, 2006, pp 1–6

  26. Kulic D, Takano W, Nakamura Y (2009) Online segmentation and clustering from continuous observation of whole body motions. IEEE Trans Robot 25(5):1158–1166

    Article  Google Scholar 

  27. Aloimonos Y, Guerra G, Ogale A (2009) The language of action: a new tool for human-centric interfaces. In: Aghajan H, López-Cózar Delgado R, Augusto JC (eds) Human-centric interfaces for ambient intelligence. Elsevier, Amsterdam. ISBN 978-0-12-374708-2

    Google Scholar 

  28. Aloimonos Y (2009) Sensory grammars for sensor networks. J Ambient Intell Smart Environ 1(1):15–21. ISSN 1876-1364

    Google Scholar 

  29. POETICON. The poetics of everyday life: Grounding resources and mechanisms for artificial agents. http://www.poeticon.eu

  30. Azad P, Ude A, Asfour T, Dillmann R (2007) Stereo-based markerless human motion capture for humanoid robot systems. In: IEEE international conference on robotics and automation, ICRA, Rome, Italy, April 2007, pp 3951–3956

  31. Azad P, Asfour T, Dillmann R (2008) Robust real-time stereo-based markerless human motion capture. In: IEEE/RAS international conference on humanoid robots, Humanoids, Daejeon, Korea, December 2008, pp 700–707

  32. Azad P (2008) Visual perception for manipulation and imitation in humanoid robots. PhD thesis, Universität Karlsruhe, TH, Karlsruhe, Germany

  33. Deutscher J, Blake A, Reid I (2000) Articulated body motion capture by annealed particle filtering. In: IEEE computer society conference on computer vision and pattern recognition, CVPR, Hilton Head, USA, pp 2126–2133

  34. Azad P, Ude A, Asfour T, Dillmann R (2007) Toward an unified representation for imitation of human motion on humanoids. In: IEEE international conference on robotics and automation, ICRA, Rome, Italy, April 2007, pp 2558–2563

  35. Pastor P, Hoffmann H, Asfour T, Schaal S (2009) Learning and generalization of motor skills by learning from demonstration. In: Proceedings of the IEEE international conference on robotics and automation, Kobe, Japan

  36. Rogalla O (2002) Abbildung von Benutzerdemonstrationen auf variable Roboterkonfigurationen. PhD thesis, Universität Karlsruhe

  37. Arbib MA, Iberall T, Lyons D (1985) Coordinated control programs for movements of the hand. Exp Brain Res 10:111–129

    Google Scholar 

  38. Jaekel R, Schmidt-Rohr SR, Xue Z, Loesch M, Dillmann R (2010) Learning of probabilistic grasping strategies using programming by demonstration. In: IEEE international conference on robotics and automation, ICRA’10, May 2010

  39. Calinon S, Billard A (2008) A probabilistic programming by demonstration framework handling constraints in joint space and task space. In: IEEE/RSJ intl conf. on intelligent robots and systems, IROS

  40. Jaekel R, Schmidt-Rohr SR, Loesch M, Dillmann R (2010) Representation and constrained planning of manipulation strategies in the context of programming by demonstration. In: IEEE international conference on robotics and automation, ICRA’10, May 2010

  41. Kasper A, Becher R, Steinhaus P, Dillmann R (2007) Developing and analyzing intuitive modes for interactive object modeling. In: ICMI’07: Proceedings of the nineth international conference on multimodal interfaces, New York, NY, USA. ACM, New York, pp 74–81

    Chapter  Google Scholar 

  42. Becher R, Steinhaus P, Zöllner R, Dillmann R (2006) Design and implementation of an interactive object modelling system. In: Robotik/ISR, München, Germany, May 2006

Download references

Author information

Authors and Affiliations

  1. Adenauerring 2, 76131, Karlsruhe, Germany

    Rüdiger Dillmann, Tamim Asfour, Martin Do, Rainer Jäkel, Alexander Kasper, Pedram Azad, Sven R. Schmidt-Rohr & Martin Lösch

  2. Jamova cesta 39, 1000, Ljubljana, Slovenia

    Aleš Ude

Authors
  1. Rüdiger Dillmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tamim Asfour
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Martin Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rainer Jäkel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexander Kasper
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Pedram Azad
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Aleš Ude
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sven R. Schmidt-Rohr
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Martin Lösch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tamim Asfour.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Dillmann, R., Asfour, T., Do, M. et al. Advances in Robot Programming by Demonstration. Künstl Intell 24, 295–303 (2010). https://doi.org/10.1007/s13218-010-0060-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13218-010-0060-0

Keywords

Search

Navigation