Abstract
For much of the history of robotics, robots have been built from inert, inorganic, bulk material, such as metals, plastics, and ceramics. However, advances in materials science are driving the development of soft robots made from increasingly exotic but still inorganic materials. Similarly, synthetic biology has recently provided the ability to build ‘biobots’ completely from biological materials. This is driven new use cases for mobile robots, but it is also allowing new questions to be posed about how both body plan and neural control jointly facilitate the evolution of intelligent behavior.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Blackiston D, Lederer E, Kriegman S, Garnier S, Bongard J (2021) Levin M A cellular platform for the development of synthetic living machines. Sci Robot 6(52):eabf1571
Bongard J (2009) Accelerating self-modeling in cooperative robot teams. IEEE Trans Evol Comput 13(2):321–332
Bongard J, Levin M (2022) There’s plenty of room right here: Biological systems as evolved, overloaded, multi-scale machines. arXiv preprint arXiv:2212.10675
Bongard J, Zykov V, Lipson H (2006) Resilient machines through continuous self-modeling. Science 314:1118–1121
Bongard JC (2011) Morphological and environmental scaffolding synergize when evolving robot controllers: artificial life/robotics/evolvable hardware. In: Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 179–186. ACM
Cully A, Clune J, Tarapore D, Mouret JB (2015) Robots that can adapt like animals. Nature 521(7553):503–507
Gumuskaya G, Srivastava P, Cooper BG, Lesser H, Semegran B, Garnier S, Levin M (2022) Motile living biobots self-construct from adult human somatic progenitor seed cells. bioRxiv 34 https://doi.org/10.1101/2022.08.04.502707
Kriegman S, Blackiston D, Levin M, Bongard J (2020) A scalable pipeline for designing reconfigurable organisms. Proc Nat Acad Sci 117(4):1853–1859
Kriegman S, Blackiston D, Levin M (2021) Bongard J Kinematic self-replication in reconfigurable organisms. Proc Nat Acad Sci 118(49):e2112672
Kriegman S, Walker S, Shah D, Levin M, Kramer-Bottiglio R, Bongard J (2019) Automated shapeshifting for function recovery in damaged robots. In: Proceedings of the Robotics: Science and Systems (RSS) Conference
Lee KY, Park SJ, Matthews DG, Kim SL, Marquez CA, Zimmerman JF, Ardoña HAM, Kleber AG, Lauder GV, Parker KK (2022) An autonomously swimming biohybrid fish designed with human cardiac biophysics. Science 375(6581):639–647
Lin Z, Jiang T, Shang J (2022) The emerging technology of biohybrid micro-robots: a review. Bio-Design Manufact 11:1–26
Mazzolai B (2020) Laschi C A vision for future bioinspired and biohybrid robots. Sci Robot 5(38):eaba6893
Nygaard TF, Martin CP, Samuelsen E, Torresen J, Glette K (2018) Real-world evolution adapts robot morphology and control to hardware limitations. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 125–132
Park SJ, Gazzola M, Park KS, Park S, Di Santo V, Blevins EL, Lind JU, Campbell PH, Dauth S, Capulli AK et al (2016) Phototactic guidance of a tissue-engineered soft-robotic ray. Science 353(6295):158–162
Parsa A, Wang D, O’Hern CS, Shattuck MD, Kramer-Bottiglio R, Bongard J (2022) Evolving programmable computational metamaterials. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 122–129
Sun L, Yu Y, Chen Z, Bian F, Ye F, Sun L, Zhao Y (2020) Biohybrid robotics with living cell actuation. Chem Soc Rev 49(12):4043–4069
Acknowledgements
The work involved biobots discussed herein was sponsored by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number HR0011-18-2-0022, the Lifelong Learning Machines program from DARPA/MTO. The content of the information does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. Approved for public release; distribution is unlimited. The metamaterials work was supported by the National Science Foundation under the DMREF program (award number: 2118810).
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was presented in part as a plenary speech at the joint symposium of the 28th International Symposium on Artificial Life and Robotics, the 8th International Symposium on BioComplexity, and the 6th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Beppu, Oita, and Online, January 25–27, 2023).
About this article
Cite this article
Bongard, J. From rigid to soft to biological robots. Artif Life Robotics 28, 282–286 (2023). https://doi.org/10.1007/s10015-023-00872-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10015-023-00872-0