How morphological development can guide evolution

Kriegman, Sam; Cheney, Nick; Bongard, Josh

doi:10.1038/s41598-018-31868-7

Cited by 81 publications

(88 citation statements)

References 40 publications

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“…In effect, one can increase the likelihood of beneficial mutations, and thus overall evolvability, simply by including developmental change in populations of evolving agents. That development unlocks broader and more nuanced ranges of behavioral change compared to non-developmental programs has recently been documented [23,24].…”

Section: The Evolution Of Developmentmentioning

confidence: 99%

Combating catastrophic forgetting with developmental compression

Kriegman

Bongard

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Generally intelligent agents exhibit successful behavior across problems in several settings. Endemic in approaches to realize such intelligence in machines is catastrophic forgetting: sequential learning corrupts knowledge obtained earlier in the sequence, or tasks antagonistically compete for system resources. Methods for obviating catastrophic forgetting have sought to identify and preserve features of the system necessary to solve one problem when learning to solve another, or to enforce modularity such that minimally overlapping sub-functions contain task specific knowledge. While successful, both approaches scale poorly because they require larger architectures as the number of training instances grows, causing different parts of the system to specialize for separate subsets of the data. Here we present a method for addressing catastrophic forgetting called developmental compression. It exploits the mild impacts of developmental mutations to lessen adverse changes to previouslyevolved capabilities and 'compresses' specialized neural networks into a generalized one. In the absence of domain knowledge, developmental compression produces systems that avoid overt specialization, alleviating the need to engineer a bespoke system for every task permutation and suggesting better scalability than existing approaches. We validate this method on a robot control problem and hope to extend this approach to other machine learning domains in the future.

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Section: The Evolution Of Developmentmentioning

confidence: 99%

Combating catastrophic forgetting with developmental compression

Kriegman

Bongard

2018

Proceedings of the Genetic and Evolutionary Computation Conference

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“…The future of this line of work promises not just new robotic systems but also new science. Shapeshifting robots, recast as scientific tools, can shed new light on old biological questions about developmental plasticity, regeneration and homeostasis [21,22,25]. And, symmetrically, new theories about the mechanisms that lie at the heart of such questions can be physically instantiated and optimized in a new breed of useful, autonomous and adaptive machines.…”

Section: Metamorphosing Machinesmentioning

confidence: 99%

Automated Shapeshifting for Function Recovery in Damaged Robots

Kriegman¹,

Walker²,

Shah³

et al. 2019

Robotics: Science and Systems XV

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Fig. 1. After learning to walk, a simulated quadruped is subjected to unanticipated insult: its legs are cut off. An evolutionary algorithm searches for deformations to the postdamage structure that, when coupled with the predamage controller, result in function recovery. One of the evolved solutions (shown here) yields the spontaneous "regeneration" of the lost legs, which was manually transferred to reality (youtu.be/afOXX2r54mQ).Abstract-A robot's mechanical parts routinely wear out from normal functioning and can be lost to injury. For autonomous robots operating in isolated or hostile environments, repair from a human operator is often not possible. Thus, much work has sought to automate damage recovery in robots. However, every case reported in the literature to date has accepted the damaged mechanical structure as fixed, and focused on learning new ways to control it. Here we show for the first time a robot that automatically recovers from unexpected damage by deforming its resting mechanical structure without changing its control policy. We found that, especially in the case of "deep insult", such as removal of all four of the robot's legs, the damaged machine evolves shape changes that not only recover the original level of function (locomotion) as before, but can in fact surpass the original level of performance (speed). This suggests that shape change, instead of control readaptation, may be a better method to recover function after damage in some cases.

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“…1. This can create a new gradient in the evolutionary search space, rewarding descendants that more rapidly manifest the trait during their lifetimes [20,24] and retain it through the remainder of their lifetime [25]. Assuming such mutations exist and can be naturally selected [24], following the gradient requires incrementally reducing development in the manifold of the search space that can express variations on the trait [40].…”

Section: Introductionmentioning

confidence: 99%

“…Developmental change produces intrinsically robust systems because they evolved from designs that had to maintain adequate performance along additional dimensions of change [4,24]. Through morphological development specifically, evolution is compelled to maintain designs that are capable across a series of body plans, with different sensor-motor contingencies; and the ability to tolerate such perturbations can become inherited to some extent in descendants' behaviors [4] and morphologies [24], even when their developmental flexibility is reduced or completely removed by canalization or fabrication.…”

Section: Introductionmentioning

confidence: 99%

“…And yet, despite the ubiquity of morphological development in nature, and the adaptive advantages it seemingly confers, there are only a handful of cases reported in the literature in which a simulated robot's mechanical structure was allowed to change while it was behaving (e.g. [4,22,24,25,39]), all of which modeled morphological development as a genetically predetermined process: the environment could not influence the way in which development unfolded.…”

Section: Introductionmentioning

confidence: 99%

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Interoceptive robustness through environment-mediated morphological development

Kriegman

Cheney

Corucci³

et al. 2018

Proceedings of the Genetic and Evolutionary Computation Conference

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Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies.

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How morphological development can guide evolution

Cited by 81 publications

References 40 publications

Combating catastrophic forgetting with developmental compression

Combating catastrophic forgetting with developmental compression

Automated Shapeshifting for Function Recovery in Damaged Robots

Interoceptive robustness through environment-mediated morphological development

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