Skip to main content
SpringerLink
Log in

Directed Locomotion for Modular Robots with Evolvable Morphologies

  • Conference paper
  • First Online:
Book cover Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11101))

Included in the following conference series:

Abstract

Morphologically evolving robot systems need to include a learning period right after ‘birth’ to acquire a controller that fits the newly created body. In this paper, we investigate learning one skill in particular: walking in a given direction. To this end, we apply the HyperNEAT algorithm guided by a fitness function that balances the distance travelled in a direction and the deviation between the desired and the actually travelled directions. We validate this method on a variety of modular robots with different shapes and sizes and observe that the best controllers produce trajectories that accurately follow the correct direction and reach a considerable distance in the given test interval.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://gazebosim.org/.

References

  1. Aoi, S., Manoonpong, P., Ambe, Y., Matsuno, F., Wörgötter, F.: Adaptive control strategies for interlimb coordination in legged robots: a review. Front. Neurorobotics 11, 39 (2017)

    Article  Google Scholar 

  2. Auerbach, J., et al.: RoboGen: robot generation through artificial evolution. In: Sayama, H., Rieffel, J., Risi, S., Doursat, R., Lipson, H. (eds.) Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, pp. 136–137. The MIT Press, New York, July 2014

    Google Scholar 

  3. Auerbach, J.E., Bongard, J.C.: On the relationship between environmental and morphological complexity in evolved robots. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, pp. 521–528. GECCO 2012. ACM, New York (2012)

    Google Scholar 

  4. Beer, R.D.: The Dynamics of Brain–Body–Environment Systems: A Status Report (2008)

    Google Scholar 

  5. Bongard, J., Zykov, V., Lipson, H.: Resilient machines through continuous self-modeling. Science 314(5802), 1118–1121 (2006)

    Article  Google Scholar 

  6. Bongard, J.C.: Evolutionary robotics. Commun. ACM 56(8), 74–83 (2013)

    Article  Google Scholar 

  7. Chatterjee, S., et al.: Reinforcement learning approach to generate goal-directed locomotion of a snake-like robot with screw-drive units. In: 2014 23rd International Conference on Robotics in Alpe-Adria-Danube Region (RAAD), pp. 1–7, September 2014

    Google Scholar 

  8. Clune, J., Beckmann, B.E., Ofria, C., Pennock, R.T.: Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: 2009 IEEE Congress on Evolutionary Computation, pp. 2764–2771, May 2009

    Google Scholar 

  9. Cully, A., Clune, J., Tarapore, D., Mouret, J.B.: Robots that can adapt like animals. Nature 521, 503 (2015)

    Article  Google Scholar 

  10. Doncieux, S., Bredeche, N., Mouret, J.B., Eiben, A.: Evolutionary robotics: what, why, and where to. Front. Robot. AI 2(4) (2015)

    Google Scholar 

  11. Eiben, A., et al.: The triangle of life: evolving robots in real-time and real-space. In: Liò, P., Miglino, O., Nicosia, G., Nolfi, S., Pavone, M. (eds.) Advances In Artificial Life, ECAL 2013, pp. 1056–1063. MIT Press (2013)

    Google Scholar 

  12. Eiben, A., Kernbach, S., Haasdijk, E.: Embodied artificial evolution. Evol. Intell. 5(4), 261–272 (2012)

    Article  Google Scholar 

  13. Eiben, A., Smith, J.: From evolutionary computation to the evolution of things. Nature 521(7553), 476–482 (2015)

    Article  Google Scholar 

  14. Eiben, A.E.: In vivo veritas: towards the evolution of things. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 24–39. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10762-2_3

    Chapter  Google Scholar 

  15. Grillner, S., Wallén, P., Saitoh, K., Kozlov, A., Robertson, B.: Neural bases of goal-directed locomotion in vertebrates-an overview. Brain Res. Rev. 57(1), 2–12 (2008)

    Article  Google Scholar 

  16. Haasdijk, E., Rusu, A.A., Eiben, A.E.: HyperNEAT for locomotion control in modular robots. In: Tempesti, G., Tyrrell, A.M., Miller, J.F. (eds.) ICES 2010. LNCS, vol. 6274, pp. 169–180. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15323-5_15

    Chapter  Google Scholar 

  17. Hooper, S.L.: Central pattern generators. In: Encyclopedia of Life Sciences, pp. 1–12, April 2001. https://doi.org/10.1038/npg.els.0000032

  18. Hupkes, E., Jelisavcic, M., Eiben, A.E.: Revolve: a versatile simulator for online robot evolution. In: Sim, K., Kaufmann, P. (eds.) EvoApplications 2018. LNCS, vol. 10784, pp. 687–702. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77538-8_46

    Chapter  Google Scholar 

  19. Ijspeert, A.J.: Central pattern generators for locomotion control in animals and robots: a review. Neural Netw. 21(4), 642–653 (2008). Robotics and Neuroscience

    Article  Google Scholar 

  20. Ijspeert, A.J., Crespi, A., Ryczko, D., Cabelguen, J.M.: From swimming to walking with a salamander robot driven by a spinal cord model. Science 315(5817), 1416–1420 (2007)

    Article  Google Scholar 

  21. Jelisavcic, M., et al.: Real-world evolution of robot morphologies: a proof of concept. Artif. Life 23(2), 206–235 (2017)

    Article  Google Scholar 

  22. Jelisavcic, M., Carlo, M.D., Haasdijk, E., Eiben, A.E.: Improving RL power for on-line evolution of gaits in modular robots. In: 2016 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8, December 2016

    Google Scholar 

  23. Jelisavcic, M., Haasdijk, E., Eiben, A.: Acquiring moving skills in robots with evolvable morphologies: recent results and outlook. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, GECCO 2017 (2017)

    Google Scholar 

  24. Kamimura, A., Kurokawa, H., Yoshida, E., Murata, S., Tomita, K., Kokaji, S.: Automatic locomotion design and experiments for a modular robotic system. IEEE/ASME Trans. Mech. 10(3), 314–325 (2005)

    Article  Google Scholar 

  25. Kamimura, A., Kurokawa, H., Yoshida, E., Tomita, K., Kokaji, S., Murata, S.: Distributed adaptive locomotion by a modular robotic system, M-TRAN II. In: 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 3, pp. 2370–2377, September 2004

    Google Scholar 

  26. Kohl, N., Stone, P.: Policy gradient reinforcement learning for fast quadrupedal locomotion. In: IEEE International Conference on 2004 Proceedings of Robotics and Automation, ICRA 2004, vol. 3, pp. 2619–2624 (2004)

    Google Scholar 

  27. Marder, E., Bucher, D.: Central pattern generators and the control of rhythmic movements. Curr. Biol. 11(23), R986–R996 (2001)

    Article  Google Scholar 

  28. Matos, V., Santos, C.P.: Towards goal-directed biped locomotion: combining CPGs and motion primitives. Robot. Auton. Syst. 62(12), 1669–1690 (2014)

    Article  Google Scholar 

  29. Paul, S., Chatzilygeroudis, K., Ciosek, K., Mouret, J.B., Osborne, M.A., Whiteson, S.: Alternating optimisation and quadrature for robust control. In: The Thirty-Second AAAI Conference on Artificial Intelligence, AAAI 2018 (2018)

    Google Scholar 

  30. Pfeifer, R., Bongard, J.C.: How the Body Shapes the Way We Think: A New View of Intelligence (Bradford Books). The MIT Press, Cambridge (2006)

    Google Scholar 

  31. Roijers, D.M., Whiteson, S.: Multi-objective decision making. Synth. Lect. Artif. Intell. Mach. Learn. 11(1), 1–129 (2017)

    Article  Google Scholar 

  32. Sproewitz, A., Moeckel, R., Maye, J., Ijspeert, A.J.: Learning to move in modular robots using central pattern generators and online optimization. Int. J. Robot. Res. 27(3–4), 423–443 (2008)

    Article  Google Scholar 

  33. Stanley, K.O.: Compositional pattern producing networks: a novel abstraction of development. Genet. Program. Evolvable Mach. 8(2), 131–162 (2007)

    Article  Google Scholar 

  34. Stanley, K.O., Miikkulainen, R.: Evolving neural networks through augmenting topologies. Evol. Comput. 10(2), 99–127 (2002)

    Article  Google Scholar 

  35. Weel, B., D’Angelo, M., Haasdijk, E., Eiben, A.: Online gait learning for modular robots with arbitrary shapes and sizes. Artif. life 23(1), 80–104 (2017)

    Article  Google Scholar 

  36. Yosinski, J., Clune, J., Hidalgo, D., Nguyen, S., Zagal, J.C., Lipson, H.: Evolving robot gaits in hardware: the hyperneat generative encoding vs. parameter optimization. In: Proceedings of the 20th European Conference on Artificial Life, pp. 890–897 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, VU University Amsterdam, Amsterdam, The Netherlands

    Gongjin Lan, Milan Jelisavcic, Diederik M. Roijers, Evert Haasdijk & A. E. Eiben

Authors
  1. Gongjin Lan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Milan Jelisavcic
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diederik M. Roijers
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Evert Haasdijk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. A. E. Eiben
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gongjin Lan .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Lan, G., Jelisavcic, M., Roijers, D.M., Haasdijk, E., Eiben, A.E. (2018). Directed Locomotion for Modular Robots with Evolvable Morphologies. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11101. Springer, Cham. https://doi.org/10.1007/978-3-319-99253-2_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-99253-2_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99252-5

  • Online ISBN: 978-3-319-99253-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation