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  • Perspective
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Robots that evolve on demand

Nature Reviews Materials volume 9, pages 822–835 (2024)Cite this article

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Abstract

Now more than ever, researchers are rethinking the way robots are designed and controlled — from the algorithms that govern their actions to the very atomic structure of the materials they are made from. In this Perspective, we collect and comment on recent efforts towards multipurpose machines that use shape-morphing materials and components to adapt to changing environments. To frame our discussion, we point out biological adaptation strategies that have been adopted by robots across different sizes and timescales. This contextualization segways into the notion of adaptive morphogenesis, which is formally defined as a design strategy in which adaptive robot morphology and behaviours are realized through unified structural and actuation systems. However, since its introduction, the term has been more colloquially used to describe ‘evolution on demand’. We set out by giving examples of current systems that exhibit adaptive morphogenesis. Then, outlining projected key application areas of adaptive morphogenesis helps to scope the challenges and possibilities on the road to realizing future systems. We conclude by proposing performance metrics for benchmarking this emerging field. With this Perspective, we hope to spur dialogue among materials scientists, roboticists and biologists, and provide an objective lens through which we can analyse progress towards robots with rapidly mutable features that eclipse what is possible in biological processes.

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Fig. 1: Adaptive morphogenesis, or ‘evolution on demand’, is a robot design strategy that synthesizes evolutionary adaptations for locomotion in different environments into a unified mechanism space.
Fig. 2: Examples of robots that exhibit adaptive morphogenesis.
Fig. 3: Future applications of AM robots.

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Acknowledgements

The authors thank L. Ramirez for collecting the AQs for ART. R.B., F.F. and R.K.-B. were supported by the Office of Naval Research under awards N00014-21-1-2417 and N00014-24-1-2162. R.K.-B. was also supported by the National Science Foundation under award CMMI-2118988, and J.B. was supported by CMMI-2118810. R.B. was also supported by The Branco Weiss Fellowship — Society in Science, administered by ETH Zürich.

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  1. School of Engineering & Applied Science, Yale University, New Haven, CT, USA

    Robert Baines & Rebecca Kramer-Bottiglio

  2. Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

    Robert Baines

  3. Department of Biology, West Chester University, West Chester, PA, USA

    Frank Fish

  4. Department of Computer Science, University of Vermont, Burlington, VT, USA

    Josh Bongard

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R.B. formulated the scope of the article and led the writing. R.K.-B. provided editorial feedback and editing at all stages. F.F. and J.B. contributed to writing and editing the manuscript.

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Correspondence to Rebecca Kramer-Bottiglio.

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Glossary

Autoencoder

A type of neural network that compresses input data into a lower-dimensional representation and then reconstructs the original data from that compressed form.

Behavioural control policy

The way a robot moves and adapts its body to accomplish a task.

Central pattern generators

(CPGs). Robot control schemes modelled on animals’ spinal cords that generate rhythmic and repeated actuation signals.

Darwin’s finches

A group of bird species with diverse beak shapes and functions; classical example of how organisms adapt over time to their environments.

Differentiable physics engines

Simulations in which all physical variables may be differentiated, enabling use of gradient-based machine learning techniques.

Functional vias

Vasculature in a robot facilitating sensing, actuation, control or power, through transport and distribution of material(s).

Hygromorphic

Swelling in response to humidity changes (as does wood, for example).

Phenotypic plasticity

The ability of organisms to adapt their body properties in response to changing environmental conditions (an example of which is the development of muscle with repeated exercise).

Pseudopodia

An offshoot from the body of a eukaryotic cell formed to facilitate movement or to ensnare food.

Reinforcement learning

(RL). Machine learning approach to teach an agent how to take actions in an environment to maximize a reward.

Simulation-to-reality (sim2real) gap

The disparity in performance between an agent in simulation and an agent physically deployed in the real world.

Transformer neural networks

A type of neural network that uses an attention mechanism to efficiently process sequential data.

Ultrastability

The ability of a system to maintain function, in spite of environmental changes, by modifying the dynamics between itself and its surroundings.

Zero-shot transfer

Direct sim2real transfer without any tuning or iteration.

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Baines, R., Fish, F., Bongard, J. et al. Robots that evolve on demand. Nat Rev Mater 9, 822–835 (2024). https://doi.org/10.1038/s41578-024-00711-z

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