Autonomous Robots

, Volume 31, Issue 2–3, pp 155–181 | Cite as

Toward simple control for complex, autonomous robotic applications: combining discrete and rhythmic motor primitives

  • Sarah DegallierEmail author
  • Ludovic Righetti
  • Sebastien Gay
  • Auke Ijspeert


Vertebrates are able to quickly adapt to new environments in a very robust, seemingly effortless way. To explain both this adaptivity and robustness, a very promising perspective in neurosciences is the modular approach to movement generation: Movements results from combinations of a finite set of stable motor primitives organized at the spinal level. In this article we apply this concept of modular generation of movements to the control of robots with a high number of degrees of freedom, an issue that is challenging notably because planning complex, multidimensional trajectories in time-varying environments is a laborious and costly process. We thus propose to decrease the complexity of the planning phase through the use of a combination of discrete and rhythmic motor primitives, leading to the decoupling of the planning phase (i.e. the choice of behavior) and the actual trajectory generation. Such implementation eases the control of, and the switch between, different behaviors by reducing the dimensionality of the high-level commands. Moreover, since the motor primitives are generated by dynamical systems, the trajectories can be smoothly modulated, either by high-level commands to change the current behavior or by sensory feedback information to adapt to environmental constraints. In order to show the generality of our approach, we apply the framework to interactive drumming and infant crawling in a humanoid robot. These experiments illustrate the simplicity of the control architecture in terms of planning, the integration of different types of feedback (vision and contact) and the capacity of autonomously switching between different behaviors (crawling and simple reaching).


CPGs Motor primitive Adaptive behaviors Dynamical systems Humanoid robots Bio-Inspiration Drumming Locomotion 


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  1. Bernstein, N. (1967). The co-ordination and regulation of movements. London: Pergamon. Google Scholar
  2. Bizzi, E., Accornero, N., Chapple, W., & Hogan, N. (1984). Posture control and trajectory formation during arm movement. The Journal of Neuroscience, 4(11), 2738–2744. Google Scholar
  3. Bizzi, E., Cheung, V. C. K., d’Avella, A., Saltiel, P., & Tresch, M. (2008). Combining modules for movement. Brain Research Reviews, 57(1), 125–33. CrossRefGoogle Scholar
  4. Buchli, J., & Ijspeert, A. J. (2008). Self-organized adaptive legged locomotion in a compliant quadruped robot. Autonomous Robots, 25(4), 331–347. CrossRefGoogle Scholar
  5. Buchli, J., Righetti, L., & Ijspeert, A. (2008). Frequency analysis with coupled nonlinear oscillators. Physica D, 237, 1705–1718. MathSciNetzbMATHCrossRefGoogle Scholar
  6. Bullock, D., & Grossberg, S. (1988). The VITE model: a neural command circuit for generating arm and articulator trajectories. In J. Kelso, A. Mandell, & M. Shlesinger (Eds.), Dynamic patterns in complex systems (pp. 206–305). Singapore: World Scientific. Google Scholar
  7. Capaday, C. (2002). The special nature of human walking and its neural control. Trends in Neurosciences, 25(7), 370–376. CrossRefGoogle Scholar
  8. Cui, X., Zhu, Y., Zang, X., Tang, S., & Zhao, J. (2010). CPG based locomotion control of pitch-yaw connecting modular self-reconfigurable robots. In Information and automation (ICIA), 2010 IEEE international conference on (pp. 1410–1415). CrossRefGoogle Scholar
  9. De Rugy, A., & Sternad, D. (2003). Interaction between discrete and rhythmic movements: reaction time and phase of discrete movement initiation during oscillatory movements. Brain Research, 994(2), 160–174. CrossRefGoogle Scholar
  10. Degallier, S., & Ijspeert, A. (2010). Modeling discrete and rhythmic movements through motor primitives: a review. Biological Cybernetics, 103(4), 319–338. CrossRefGoogle Scholar
  11. Degallier, S., Santos, C. P., Righetti, L., & Ijspeert, A. (2006). Movement generation using dynamical systems: a humanoid robot performing a drumming task. In IEEE-RAS inter. conf. on humanoid robots (pp. 512–517). CrossRefGoogle Scholar
  12. Degallier, S., Righetti, L., & Ijspeert, A. (2007). Hand placement during quadruped locomotion in a humanoid robot: a dynamical system approach. In IEEE-RAS international conference on intelligent robots and systems (IROS07). Google Scholar
  13. Degallier, S., Righetti, L., Natale, L., Nori, F., Metta, G., & Ijspeert, A. (2008). A modular bio-inspired architecture for movement generation for the infant-like robot icub. In Proceedings of the second IEEE RAS/EMBS international conference on biomedical robotics and biomechatronics, BioRob. Google Scholar
  14. Fitts, P. (1954). The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology, 47(6), 381–391. CrossRefGoogle Scholar
  15. Fitzpatrick, P., Metta, G., & Natale, L. (2008). Towards long-lived robot genes. Robotics and Autonomous Systems, 56(1), 29–45. CrossRefGoogle Scholar
  16. Frigon, S., & Rossignol, S. (2006). Experiments and models of sensorimotor interactions during locomotion. Biological Cybernetics, 95(6), 607–627. zbMATHCrossRefGoogle Scholar
  17. Gay, S., Degallier, S., Pattacini, U., Ijspeert, A., & Santos, J. (2010). Integration of vision and central pattern generator based locomotion for path planning of a nonholonomic crawling humanoid robot. In Proceedings of the 2010 IEEE/RSJ international conference on intelligent robots and systems (IROS 2010), Taipei. Google Scholar
  18. Gribovskaya, E., & Billard, A. (2008). Combining dynamical systems control and programming by demonstration for teaching discrete bimanual coordination tasks to a humanoid robot. In Proceedings of 3rd ACM/IEEE international conference on human-robot interaction, HRI’08, Amsterdam, 12–15 March 2008. Google Scholar
  19. Grillner, S. (2006). Biological pattern generation: the cellular and computational logic of networks in motion. Neuron, 52(5), 751–766. CrossRefGoogle Scholar
  20. Hersch, M., & Billard, A. (2008). Reaching with multi-referential dynamical systems. Autonomous Robots, 25(1–2), 71–83. CrossRefGoogle Scholar
  21. Ijspeert, A., Nakanishi, J., & Schaal, S. (2002). Learning rhythmic movements by demonstration using nonlinear oscillators. In Proceedings of the IEEE/RSJ int. conference on intelligent robots and systems (IROS2002) (pp. 958–963). Google Scholar
  22. Ijspeert, A. J., Nakanishi, J., & Schaal, S. (2003). Learning attractor landscapes for learning motor primitives. In S. T. Becker & K. Obermayer (Eds.), Neural information processing systems 15 (NIPS2002) (pp. 1547–1554). Google Scholar
  23. Kalakrishnan, M., Buchli, J., Pastor, P., Mistry, M., & Schaal, S. (2010). Fast, robust quadruped locomotion over challenging terrain. In IEEE international conference on robotics and automation (ICRA10). Google Scholar
  24. Kelso, J. A. S., Southard, D. L., & Goodman, D. (1979). On the nature of human interlimb coordination. Science, 203(4384), 1029–1031. CrossRefGoogle Scholar
  25. Khatib, O. (1980). Commande dynamique dans l’espace opérationnel des robots manipulateurs en présence d’obstacles. PhD thesis, Ecole Nationale Supérieure de l’aéronautique et de l’espace, Toulouse, France. Google Scholar
  26. Khatib, O. (1986). Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, 5(1), 90–98. MathSciNetCrossRefGoogle Scholar
  27. Kimura, H., Fukuoka, Y., & Cohen, A. H. (2007). Adaptive dynamic walking of a quadruped robot on natural ground based on biological concepts. The International Journal of Robotics Research, 26(5), 475–490. CrossRefGoogle Scholar
  28. Kober, J., & Peters, J. (2010). Imitation and reinforcement learning. IEEE Robotics & Automation Magazine, 17(2), 55–62. CrossRefGoogle Scholar
  29. Kose-Bagci, H., Dautenhahn, K., Syrdal, D. S., & Nehaniv, C. L. (2010). Drum-mate: interaction dynamics and gestures in human-humanoid drumming experiments. Connection Science, 22(2), 103–134. CrossRefGoogle Scholar
  30. Matsuoka, K. (1985). Sustained oscillations generated by mutually inhibiting neurons with adaptation. Biological Cybernetics, 52, 367–376. MathSciNetzbMATHCrossRefGoogle Scholar
  31. Maufroy, C., Kimura, H., & Takase, K. (2008). Towards a general neural controller for quadrupedal locomotion. Neural Networks, 21(4), 667–681. CrossRefGoogle Scholar
  32. Michel, O. (2004). Webots tm: Professional mobile robot simulation. International Journal of Advanced Robotic Systems, 1, 39–42. Google Scholar
  33. Pastor, P., Hoffmann, H., Asfour, T., & Schaal, S. (2009). Learning and generalization of motor skills by learning from demonstration. In International conference on robotics and automation (ICRA 2009). Google Scholar
  34. Righetti, L. (2008). Control of legged locomotion using dynamical systems. PhD thesis, EPFL, Lausanne. Google Scholar
  35. Righetti, L., & Ijspeert, A. (2006a). Design methodologies for central pattern generators: an application to crawling humanoids. In Proceedings of robotics: science and systems, Philadelphia, USA. Google Scholar
  36. Righetti, L., & Ijspeert, A. (2006b). Programmable central pattern generators: an application to biped locomotion control. In Proceedings of the 2006 IEEE international conference on robotics and automation. Google Scholar
  37. Righetti, L., & Ijspeert, A. (2008). Pattern generators with sensory feedback for the control of quadruped locomotion. In Proceedings of the 2008 IEEE international conference on robotics and automation (ICRA 2008) (pp. 819–824). Google Scholar
  38. Righetti, L., Buchli, J., & Ijspeert, A. (2006). Dynamic hebbian learning in adaptive frequency oscillators. Physica D, 216(2), 269–281. MathSciNetzbMATHCrossRefGoogle Scholar
  39. Ronsse, R., Sternad, D., & Lefèvre, P. (2009). A computational model for rhythmic and discrete movements in uni- and bimanual coordination. Neural Computation, 21(5), 1335–1370. MathSciNetzbMATHCrossRefGoogle Scholar
  40. Ronsse, R., Vitiello, N., Lenzi, T., van den Kieboom, J., Carrozza, M., & Ijspeert, A. (2010). Human-robot synchrony: flexible assistance using adaptive oscillators. IEEE Transactions on Biomedical Engineering, (99), 1. doi: 10.1109/TBME.2010.2089629
  41. Schaal, S., Kotosaka, S., & Sternad, D. (2000). Nonlinear dynamical systems as movement primitives. In International conference on humanoid robotics (Humanoids00) (pp. 117–124). Berlin: Springer. Google Scholar
  42. Schoener, G. (1990). A dynamic theory of coordination of discrete movement. Biological Cybernetics, 63, 257–270. MathSciNetCrossRefGoogle Scholar
  43. Schoener, G., & Kelso, J. A. S. (1988). Dynamic pattern generation in behavioral and neural systems. Science, 239(4847), 1513–1520. CrossRefGoogle Scholar
  44. Schoener, G., & Santos, C. (2001). Control of movement time and sequential action through attractor dynamics: a simulation study demonstrating object interception and coordination. In Neurons, networks, and motor behavior. Google Scholar
  45. Schoener, G., Dose, M., & Engels, C. (1995). Dynamics of behavior: theory and applications for autonomous robot architectures. Robotics and Autonomous Systems, 16(2–4), 213–245. CrossRefGoogle Scholar
  46. Sentis, L., & Khatib, O. (2005). Synthesis of whole-body behaviors through hierarchical control of behavioral primitives. International Journal of Humanoid Robotics, 2(4), 505–518. CrossRefGoogle Scholar
  47. Sproewitz, A., Pouya, S., Bonardi, S., van den Kieboom, J., Moeckel, R., Billard, A., Dillenbourg, P., & Ijspeert, A. (2010). Roombots: reconfigurable robots for adaptive furniture. IEEE Computational Intelligence Magazine, special issue on “Evolutionary and developmental approaches to robotics”. Google Scholar
  48. Steinhage, A., & Bergener, T. (1998). Dynamical systems for the behavioral organization of an anthropomorphic mobile robot. In Proceedings of the fifth international conference on simulation of adaptive behavior on from animals to animats 5 (pp. 147–152). Cambridge: MIT Press. Google Scholar
  49. Tsagarakis, N., Metta, G., Sandini, G., Vernon, D., Beira, R., Becchi, F., Righetti, L., Santos-Victor, J., Ijspeert, A., Carrozza, M., & Caldwell, D. (2007). iCub—the design and realization of an open humanoid platform for cognitive and neuroscience research. International Journal of Advanced Robotics, 21(10), 1151–1175. Special Issue on Robotic platforms for Research in Neuroscience. CrossRefGoogle Scholar
  50. Tuma, M., Iossifidis, I., & Schoner, G. (2009). Temporal stabilization of discrete movement in variable environments: an attractor dynamics approach. In Robotics and automation, 2009. ICRA ’09. IEEE international conference on (pp. 863–868). CrossRefGoogle Scholar
  51. Turvey, M. (1990). Coordination. The American Psychologist, 45(8), 938–953. CrossRefGoogle Scholar
  52. Ude, A., Gams, A., Asfour, T., & Morimoto, J. (2010). Task-specific generalization of discrete and periodic dynamic movement primitives. IEEE Transactions on Robotics, 26(5), 800–815. CrossRefGoogle Scholar
  53. Wächter, A., & Biegler, L. T. (2006). On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming. Mathematical Programming, 106, 25–57. MathSciNetzbMATHCrossRefGoogle Scholar
  54. Wagner, D., & Schmalstieg, D. (2007). Artoolkitplus for pose tracking on mobile devices. In Proceedings of 12th computer vision winter workshop (CVWW’07). Google Scholar
  55. Williamson, M. (1999). Robot arm control exploiting natural dynamics. PhD thesis, MIT Department of Electrical Engineering and Computer Science. Google Scholar
  56. Won, J., & Hogan, N. (1995). Stability properties of human reaching movements. Experimental Brain Research, 107(1), 125–136. CrossRefGoogle Scholar
  57. Zico Kolter, J., & Ng, A. Y. (2009). Task-space trajectories via cubic spline optimization. In Proceedings of the 2009 IEEE international conference on robotics and automation, Kobe, Japan (pp. 2364–2371). New York: IEEE Press. Google Scholar
  58. Zucker, M., Bagnell, J. A. D., Atkeson, C., & Kuffner, J. (2010). An optimization approach to rough terrain locomotion. In IEEE conference on robotics and automation. Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Sarah Degallier
    • 1
    Email author
  • Ludovic Righetti
    • 3
  • Sebastien Gay
    • 2
  • Auke Ijspeert
    • 2
  1. 1.CNBI Laboratory, School of EngineeringEPFL Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Biorobotics Laboratory, School of EngineeringEPFL Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.Computational Learning and Motor Control Lab, Computer Science, Neurosciences, & Biomedical EngineeringUniversity of Southern CaliforniaLos AngelesUSA

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