On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme

Goff, Léni K. Le; Hart, Emma

doi:10.1145/3449726.3463156

“…Learning allows to fine-tune the coupling between body and brain and also allows for more degrees of freedom that account for environmental changes. The majority of such research consists of applying learning algorithms to the evolvable brains of robots with fixed bodies [ [16][17][18][19][20][21][22][23][24][25] ], but some have also evolved the morphologies [ [26][27][28][29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Here again, the majority of related work consists of applying learning algorithms to the evolvable brains of robots with fixed bodies [17][18][19][20][21][22][23][24][25][26] . However, it has been argued that morphological robot evolution must include a learning stage immediately after reproduction 27 , and some recent studies have investigated the combination of body evolution, brain evolution, and learning [28][29][30][31][32][33][34][35][36] .Further to being a bio-inspired technique for developing robots, ER forms an alternative approach to studying issues in evolutionary biology. An evolving robot system, be it real or simulated, can be considered as a model of an evolving system of living organisms and used to test hypotheses experimentally 37 .…”

mentioning

confidence: 99%

“…Here again, the majority of related work consists of applying learning algorithms to the evolvable brains of robots with fixed bodies [17][18][19][20][21][22][23][24][25][26] . However, it has been argued that morphological robot evolution must include a learning stage immediately after reproduction 27 , and some recent studies have investigated the combination of body evolution, brain evolution, and learning [28][29][30][31][32][33][34][35][36] .…”

mentioning

confidence: 99%

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Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

Luo,

Miras,

Tomczak

et al. 2023

Preprint

0

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Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question ''What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?'' We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of 'morphological intelligence', the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: 'newborn' robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.

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“…Here again, the majority of related work consists of applying learning algorithms to the evolvable brains of robots with fixed bodies [17][18][19][20][21][22][23][24][25][26] . However, it has been argued that morphological robot evolution must include a learning stage immediately after reproduction 27 , and some recent studies have investigated the combination of body evolution, brain evolution, and learning [28][29][30][31][32][33][34][35][36] .Further to being a bio-inspired technique for developing robots, ER forms an alternative approach to studying issues in evolutionary biology. An evolving robot system, be it real or simulated, can be considered as a model of an evolving system of living organisms and used to test hypotheses experimentally 37 .…”

mentioning

confidence: 99%

“…Here again, the majority of related work consists of applying learning algorithms to the evolvable brains of robots with fixed bodies [17][18][19][20][21][22][23][24][25][26] . However, it has been argued that morphological robot evolution must include a learning stage immediately after reproduction 27 , and some recent studies have investigated the combination of body evolution, brain evolution, and learning [28][29][30][31][32][33][34][35][36] .…”

mentioning

confidence: 99%

Enhancing robot evolution through Lamarckian principles

Luo,

Miras,

Tomczak

et al. 2023

Sci Rep

0

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Evolutionary robot systems offer two principal advantages: an advanced way of developing robots through evolutionary optimization and a special research platform to conduct what-if experiments regarding questions about evolution. Our study sits at the intersection of these. We investigate the question “What if the 18th-century biologist Lamarck was not completely wrong and individual traits learned during a lifetime could be passed on to offspring through inheritance?” We research this issue through simulations with an evolutionary robot framework where morphologies (bodies) and controllers (brains) of robots are evolvable and robots also can improve their controllers through learning during their lifetime. Within this framework, we compare a Lamarckian system, where learned bits of the brain are inheritable, with a Darwinian system, where they are not. Analyzing simulations based on these systems, we obtain new insights about Lamarckian evolution dynamics and the interaction between evolution and learning. Specifically, we show that Lamarckism amplifies the emergence of ‘morphological intelligence’, the ability of a given robot body to acquire a good brain by learning, and identify the source of this success: newborn robots have a higher fitness because their inherited brains match their bodies better than those in a Darwinian system.

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Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

Hart

¹

,

Goff

²

2021

Phil. Trans. R. Soc. B

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We survey and reflect on how learning (in the form of individual learning and/or culture) can augment evolutionary approaches to the joint optimization of the body and control of a robot. We focus on a class of applications where the goal is to evolve the body and brain of a single robot to optimize performance on a specified task. The review is grounded in a general framework for evolution which permits the interaction of artificial evolution acting on a population with individual and cultural learning mechanisms. We discuss examples of variations of the general scheme of ‘evolution plus learning’ from a broad range of robotic systems, and reflect on how the interaction of the two paradigms influences diversity, performance and rate of improvement. Finally, we suggest a number of avenues for future work as a result of the insights that arise from the review. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

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On the challenges of jointly optimising robot morphology and control using a hierarchical optimisation scheme

Cited by 6 publications

References 12 publications

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

Lamarck's Revenge: Inheritance of Learned Traits Can Make Robot Evolution Better

Enhancing robot evolution through Lamarckian principles

Artificial evolution of robot bodies and control: on the interaction between evolution, learning and culture

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