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SOFT ROBOTICS

Resilient machines through adaptive morphology

Nature Machine Intelligence volume 1, pages 338–339 (2019)Cite this article

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To prepare robots for working autonomously under real-world conditions, their resilience and capability to recover from damage needs to improve radically. A fresh take on robot design suggests that instead of adapting the robotic control strategy, we could enable robots to change their physical bodies to recover more effectively from damage.

Despite the success story of industrial robotics, so far, the research community has not been able to fully translate these achievements into truly autonomous robots working in open world scenarios. A recent work by Sam Kriegman et al., published as part of the 15th Robotics: Science and Systems conference, suggests a key to this problem might be to allow them to adapt their physical bodies1.

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Fig. 1: A quadruped soft robot made from elastomer voxels (or cuboids).

References

  1. Kriegman, S. et al. in Proc. Robotics: Science and Systems XV 28 (Robotics Proceedings, 2019).

  2. Bongard, J., Zykov, V. & Lipson, H. Science 314, 1118–1121 (2006).

    Article  Google Scholar 

  3. Cully, A., Clune, J., Tarapore, D. & Mouret, J.-P. Nature 521, 503–507 (2015).

    Article  Google Scholar 

  4. Pfeifer, R. & Bongard, J. C. How the Body Shapes the Way We Think (MIT Press, 2006).

  5. Hauser, H., Ijspeert, A. J., Füchslin, R. M., Pfeifer, R. & Maass, W. Biol. Cybern. 105, 355–370 (2011).

    Article  Google Scholar 

  6. Hermans, M., Burm, M., Van Vaerenbergh, T., Dambre, J. & Bienstman, P. Nat. Commun. 6, 6729 (2015).

    Article  Google Scholar 

  7. Garrad, M., Rossiter, J. & Hauser, H. IEEE Robot. Autom. Lett. 3, 2056–2062 (2018).

    Article  Google Scholar 

  8. Rus, D. & Tolley, M. T. Nature 521, 467–475 (2015).

    Article  Google Scholar 

  9. McEvoy, M. A. & Correll, N. Science 347, 1261689 (2015).

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Engineering Mathematics, University of Bristol, Bristol, UK

    Helmut Hauser

  2. Softlab, Bristol Robotics Laboratory, Bristol, UK

    Helmut Hauser

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  1. Helmut Hauser
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Correspondence to Helmut Hauser.

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The author declares no competing interests.

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Hauser, H. Resilient machines through adaptive morphology. Nat Mach Intell 1, 338–339 (2019). https://doi.org/10.1038/s42256-019-0076-6

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