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Self-organized adaptation of a simple neural circuit enables complex robot behaviour

Nature Physics volume 6, pages 224–230 (2010)Cite this article

Abstract

Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Present robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors by means of a simple neural control circuit, thereby generating 11 basic behavioural patterns (for example, orienting, taxis, self-protection and various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and also enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.

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Figure 1: The six-legged walking machine AMOS-WD06 and the sensor-driven neural control set-up.
Figure 2: Control of unstable periodic orbits in the chaotic CPG module.
Figure 3: Chaos-controlled CPG generates sensor-induced behavioural patterns of the hexapod AMOS-WD06.
Figure 4: Learning sensor–motor mappings.

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Acknowledgements

We thank F. Pasemann, T. Geisel, A. Büschges and A. J. Ijspeert for fruitful discussions and acknowledge financial support by the Ministry for Education and Science (BMBF), Germany, through the Bernstein Center for Computational Neuroscience, grant numbers 01GQ0432 (F.W.) and 01GQ0430 (M.T.) as well as by the Max Planck Society (M.T.). F.W. acknowledges financial support by the European Commission ‘PACO-PLUS’.

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Authors and Affiliations

  1. Bernstein Center for Computational Neuroscience, 37073 Göttingen, Germany

    Silke Steingrube, Marc Timme, Florentin Wörgötter & Poramate Manoonpong

  2. Department of Solar Energy, Institute for Solid State Physics, ISFH/University of Hannover, 30167 Hannover, Germany

    Silke Steingrube

  3. Network Dynamics Group, Max Planck Institute for Dynamics & Self-Organization, 37073 Göttingen, Germany

    Marc Timme

  4. Faculty of Physics, University of Göttingen, 37077 Göttingen, Germany

    Marc Timme, Florentin Wörgötter & Poramate Manoonpong

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  1. Silke Steingrube
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  4. Poramate Manoonpong
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Contributions

All authors conceived and designed the experiments, contributed materials and analysis tools and analysed the data. S.St. carried out the numerical experiments. P.M. developed the robotic system. P.M. and S.St. carried out the robotic experiments. M.T., F.W. and S.St. worked out the theory. M.T. and F.W. supervised the numerical and robotic experiments. M.T., F.W. and P.M. wrote the manuscript.

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Correspondence to Poramate Manoonpong.

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The authors declare no competing financial interests.

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Steingrube, S., Timme, M., Wörgötter, F. et al. Self-organized adaptation of a simple neural circuit enables complex robot behaviour. Nature Phys 6, 224–230 (2010). https://doi.org/10.1038/nphys1508

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