Abstract
Many organisms, including various species of spiders and caterpillars, change their shape to switch gaits and adapt to different environments. Recent technological advances, ranging from stretchable circuits to highly deformable soft robots, have begun to make shape-changing robots a possibility. However, it is currently unclear how and when shape change should occur, and what capabilities could be gained, leading to a wide range of unsolved design and control problems. To begin addressing these questions, here we manually design, simulate, and build a soft robot that utilizes shape change to achieve locomotion over both a flat and inclined surface. Modelling this robot in simulation, we explore its capabilities in two environments and demonstrate the existence of environment-specific shapes and gaits that successfully transfer to the physical hardware. We found that the shape-changing robot traverses these environments better than an equivalent but non-morphing robot, in simulation and reality.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Data availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Code availability
A public repository at https://github.com/jpp46/NATURE_MI2020 contains the code necessary to reproduce the soft-robot simulations.
Change history
01 April 2024
A Correction to this paper has been published: https://doi.org/10.1038/s42256-024-00814-w
References
Jager, P. Cebrennus Simon, 1880 (Araneae: Sparassidae): a revisionary up-date with the description of four new species and an updated identification key for all species. Zootaxa 3790, 319–356 (2014).
Bhanoo, S. N. A desert spider with astonishing moves. The New York Times D4 (2014).
Armour, R. H. & Vincent, J. F. V. Rolling in nature and robotics: a review. J. Bionic Eng. 3, 195–208 (2006).
Lin, H.-T., Leisk, G. G. & Trimmer, B. GoQBot: a caterpillar-inspired soft-bodied rolling robot. Bioinspir. Biomim. 6, 026007 (2011).
Christensen, D. J. Evolution of shape-changing and self-repairing control for the atron self-reconfigurable robot. In Proc. 2006 IEEE International Conference on Robotics and Automation (ICRA) 2539–2545 (IEEE, 2006).
Yim, M. et al. Modular self-reconfigurable robot systems [grand challenges of robotics]. IEEE Robot. Autom. Mag. 14, 43–52 (2007).
Parrott, C., Dodd, T. J. & Groß, R. HyMod: A 3-DOF Hybrid Mobile and Self-Reconfigurable Modular Robot and its Extensions. In Distributed Autonomous Robotic Systems (eds. Groß, R. et al.) 401–414 (Springer, 2018).
Paul, C., Valero-Cuevas, F. J. & Lipson, H. Design and control of tensegrity robots for locomotion. IEEE Trans. Robot. 22, 944–957 (2006).
Sabelhaus, A. P. et al. System design and locomotion of superball, an untethered tensegrity robot. In 2015 IEEE International Conference on Robotics and Automation (ICRA) 2867–2873 (IEEE, 2015).
Sadeghi, A., Mondini, A. & Mazzolai, B. Toward self-growing soft robots inspired by plant roots and based on additive manufacturing technologies. Soft Robot. 4, 211–223 (2017).
Miyashita, S., Guitron, S., Ludersdorfer, M., Sung, C. R. & Rus, D. An untethered miniature origami robot that self-folds, walks, swims, and degrades. In 2015 IEEE International Conference on Robotics and Automation (ICRA) 1490–1496 (IEEE, 2015).
Rus, D. & Tolley, M. T. Design, fabrication and control of origami robots. Nat. Rev. Mater. 3, 101–112 (2018).
Pfeifer, R., Lungarella, M. & Iida, F. Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–1093 (2007).
Saranli, U., Buehler, M. & Koditschek, D. E. Rhex: a simple and highly mobile hexapod robot. Int. J. Robot. Res. 20, 616–631 (2001).
Raibert, M., Blankespoor, K., Nelson, G. & Playter, R. BigDog, the rough-terrain quadruped robot. IFAC Proc. Vol. 41, 10822–10825 (2008).
Kuindersma, S. et al. Optimization-based locomotion planning, estimation, and control design for the atlas humanoid robot. Auton. Robot. 40, 429–455 (2016).
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, 1416–1420 (2007).
Li, M., Guo, S., Hirata, H. & Ishihara, H. Design and performance evaluation of an amphibious spherical robot. Robot. Auton. Syst. 64, 21–34 (2015).
Myeong, W. C., Jung, K. Y., Jung, S. W., Jung, Y. & Myung, H. Development of a drone-type wall-sticking and climbing robot. In 2015 12th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) 386–389 (IEEE, 2015).
Bachmann, R. J., Boria, F. J., Vaidyanathan, R., Ifju, P. G. & Quinn, R. D. A biologically inspired micro-vehicle capable of aerial and terrestrial locomotion. Mech. Mach. Theory 44, 513–526 (2009).
Roderick, W. R., Cutkosky, M. R. & Lentink, D. Touchdown to take-off: at the interface of flight and surface locomotion. Interface Focus 7, 20160094 (2017).
Korayem, M. H., Tourajizadeh, H. & Bamdad, M. Dynamic load carrying capacity of flexible cable suspended robot: robust feedback linearization control approach. J. Intell. Robot. Syst. 60, 341–363 (2010).
Li, J., Ma, H., Yang, C. & Fu, M. Discrete-time adaptive control of robot manipulator with payload uncertainties. In 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER) 1971–1976 (IEEE, 2015).
Bongard, J., Zykov, V. & Lipson, H. Resilient machines through continuous self-modeling. Science 314, 1118–1121 (2006).
Cully, A., Clune, J., Tarapore, D. & Mouret, J.-B. Robots that can adapt like animals. Nature 521, 503–507 (2015).
Chatzilygeroudis, K., Vassiliades, V. & Mouret, J.-B. Reset-free trial-and-error learning for robot damage recovery. Robot. Auton. Syst. 100, 236–250 (2018).
Rosendo, A., von Atzigen, M. & Iida, F. The trade-off between morphology and control in the co-optimized design of robots. PLoS ONE 12, e0186107 (2017).
Garrad, M., Rossiter, J. & Hauser, H. Shaping behavior with adaptive morphology. IEEE Robot. Autom. Lett. 3, 2056–2062 (2018).
Hauser, H. Resilient machines through adaptive morphology. Nat. Mach. Intell. 1, 338–339 (2019).
Yim, S. & Sitti, M. Shape-programmable soft capsule robots for semi-implantable drug delivery. IEEE Trans. Robot. 28, 1198–1202 (2012).
Shah, D. S., Yuen, M. C.-S., Tilton, L. G., Yang, E. J. & Kramer-Bottiglio, R. Morphing robots using robotic skins that sculpt clay. IEEE Robot. Autom. Lett. 4, 2204–2211 (2019).
Lee, D.-Y., Kim, S.-R., Kim, J.-S., Park, J.-J. & Cho, K.-J. Origami wheel transformer: a variable-diameter wheel drive robot using an origami structure. Soft Robot. 4, 163–180 (2017).
Kriegman, S. et al. Automated shapeshifting for function recovery in damaged robots. In Proc. Robotics: Science and Systems (2019).
Hiller, J. & Lipson, H. Dynamic simulation of soft multimaterial 3D-printed objects. Soft Robot. 1, 88–101 (2014).
Jakobi, N., Husbands, P. & Harvey, I. Noise and the reality gap: the use of simulation in evolutionary robotics. In European Conference on Artificial Life (eds. Morán, F. et al.) 704–720 (Springer, 1995).
Lipson, H. & Pollack, J. B. Automatic design and manufacture of robotic lifeforms. Nature 406, 974 (2000).
Koos, S., Mouret, J.-B. & Doncieux, S. The transferability approach: crossing the reality gap in evolutionary robotics. IEEE Trans. Evol. Comput. 17, 122–145 (2013).
Bartlett, N. W. et al. A 3D-printed, functionally graded soft robot powered by combustion. Science 349, 161–165 (2015).
Rusu, A. A. et al. Sim-to-real robot learning from pixels with progressive nets. In Conference on Robot Learning 262–270 (PMLR, 2017).
Chebotar, Y. et al. Closing the sim-to-real loop: adapting simulation randomization with real world experience. In 2019 International Conference on Robotics and Automation (ICRA) 8973–8979 (2019).
Peng, X. B., Andrychowicz, M., Zaremba, W. & Abbeel, P. Sim-to-real transfer of robotic control with dynamics randomization. In 2018 IEEE International Conference on Robotics and Automation (ICRA) 1–8 (IEEE, 2018).
Hwangbo, J. et al. Learning agile and dynamic motor skills for legged robots. Sci. Robot. 4, eaau5872 (2019).
Hiller, J. & Lipson, H. Automatic design and manufacture of soft robots. IEEE Trans. Robot. 28, 457–466 (2012).
Mitchell, M., Holland, J. H. & Forrest, S. in Advances in Neural Information Processing Systems 6 (eds Cowan, J. D. et al.) 51–58 (Morgan-Kaufmann, 1994).
Booth, J. W. et al. OmniSkins: robotic skins that turn inanimate objects into multifunctional robots. Sci. Robot. 3, eaat1853 (2018).
Felton, S. M., Tolley, M. T., Onal, C. D., Rus, D. & Wood, R. J. Robot self-assembly by folding: a printed inchworm robot. In 2013 IEEE International Conference on Robotics and Automation 277–282 (IEEE, 2013).
Lee, D., Kim, S., Park, Y. & Wood, R. J. Design of centimeter-scale inchworm robots with bidirectional claws. In 2011 IEEE International Conference on Robotics and Automation 3197–3204 (IEEE, 2011).
Booth, J. W., Case, J. C., White, E. L., Shah, D. S. & Kramer-Bottiglio, R. An addressable pneumatic regulator for distributed control of soft robots. In 2018 IEEE International Conference on Soft Robotics (RoboSoft) 25–30 (IEEE, 2018).
Kim, S. Y. et al. Reconfigurable soft body trajectories using unidirectionally stretchable composite laminae. Nat. Commun. 10, 3464 (2019).
Howard, D. et al. Evolving embodied intelligence from materials to machines. Nat. Mach. Intell. 1, 12–19 (2019).
Soter, G., Conn, A., Hauser, H. & Rossiter, J. Bodily aware soft robots: integration of proprioceptive and exteroceptive sensors. In 2018 IEEE International Conference on Robotics and Automation (ICRA) 2448–2453 (IEEE, 2018).
Umedachi, T., Kano, T., Ishiguro, A. & Trimmer, B. A. Gait control in a soft robot by sensing interactions with the environment using self-deformation. Open Sci. 3, 160766 (2016).
Corucci, F., Cheney, N., Giorgio-Serchi, F., Bongard, J. & Laschi, C. Evolving soft locomotion in aquatic and terrestrial environments: effects of material properties and environmental transitions. Soft Robot. 5, 475–495 (2018).
Baines, R., Freeman, S., Fish, F. & Kramer, R. Variable stiffness morphing limb for amphibious legged robots inspired by chelonian environmental adaptations. Bioinspir. Biomim. 15, 025002 (2020).
Acknowledgements
This work was supported by NSF EFRI award 1830870. D.S.S. was supported by a NASA Space Technology Research Fellowship (80NSSC17K0164). J.P.P. was supported by the Vermont Space Grant Consortium under NASA Cooperative Agreement NNX15AP86H.
Author information
Authors and Affiliations
Contributions
J.B., R.K.-B., S.K., D.S.S. and J.P.P. conceived the project and planned the experiments. J.P.P. coded the simulation and ran the evolutionary algorithm experiments. D.S.S. and L.G.T. manufactured the robot and performed the hardware experiments. D.S.S., J.P.P., L.G.T., S.K., J.B. and R.K.-B. drafted and edited the manuscript. All authors contributed to, and agree with, the content of the final version of the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Machine Intelligence thanks the anonymous reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Supplementary Information
Supplementary Figs. 1–4 and Text 1.
Supplementary Video 1
In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shah, D.S., Powers, J.P., Tilton, L.G. et al. A soft robot that adapts to environments through shape change. Nat Mach Intell 3, 51–59 (2021). https://doi.org/10.1038/s42256-020-00263-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42256-020-00263-1
- Springer Nature Limited
This article is cited by
-
Bioinspired electronics for intelligent soft robots
Nature Reviews Electrical Engineering (2024)
-
Bioinspired handheld time-share driven robot with expandable DoFs
Nature Communications (2024)
-
Advanced Design of Soft Robots with Artificial Intelligence
Nano-Micro Letters (2024)
-
Multi-Modal Mobility Morphobot (M4) with appendage repurposing for locomotion plasticity enhancement
Nature Communications (2023)
-
Self-vectoring electromagnetic soft robots with high operational dimensionality
Nature Communications (2023)