Evolving embodied intelligence from materials to machines

Abstract

Natural lifeforms specialize to their environmental niches across many levels, from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs and overarching body plans. We propose ‘multi-level evolution’, a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions. Multi-level evolution concurrently explores constituent molecular and material building blocks, as well as their possible assemblies into specialized morphological and sensorimotor configurations. Multi-level evolution provides a route to fully harness a recent explosion in available candidate materials and ongoing advances in rapid manufacturing processes. We outline a feasible architecture that realizes this vision, highlight the main roadblocks and how they may be overcome, and show robotic applications to which multi-level evolution is particularly suited. By forming a research agenda to stimulate discussion between researchers in related fields, we hope to inspire the pursuit of multi-level robotic design all the way from material to machine.

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Fig. 1: Sample MLE architecture, creation of solutions, creation of a robot and hierarchical genotype.
Fig. 2: Showing how MLE can provide a diversity of robots for a diversity of environmental niches.

Christopher Michel (Antarctica); Jean-Baptiste Mouret (Amazon); and Dimitry B. (Sahara)

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Acknowledgements

D.H., D.F.K., P.V. and D.W. would like to acknowledge Active Integrated Matter, one of CSIRO’s Future Science Platforms, for funding this research. J.-B.M. is funded by the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 637972, project ‘ResiBots’).

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All authors contributed in forming the concept of multi-level evolution, and to the writing of the Perspective. D.H., J.-B.M., P.V., D.W. and A.E contributed on the evolutionary side, and D.F.K. and D.W. on the materials side.

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Correspondence to David Howard.

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Howard, D., Eiben, A.E., Kennedy, D.F. et al. Evolving embodied intelligence from materials to machines. Nat Mach Intell 1, 12–19 (2019). https://doi.org/10.1038/s42256-018-0009-9

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