Lamarckian Evolution of Simulated Modular Robots

Jelisavcic, Milan; Glette, Kyrre; Haasdijk, Evert; Eiben, A. E.

doi:10.3389/frobt.2019.00009

Cited by 27 publications

(33 citation statements)

References 60 publications

(74 reference statements)

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“…This means that individually learned skills are coded into the genome and thus become inheritable. This requires more research, but the first results are promising [29]. Last but not least, evolution and learning are capable of delivering working controller software and perform what we call second-order software engineering.…”

Section: Discussionmentioning

confidence: 99%

Towards Autonomous Robot Evolution

Eiben

Hart

Timmis

et al. 2020

Software Engineering for Robotics

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We outline a perspective on the future of evolutionary robotics and discuss a long-term vision regarding robots that evolve in the real world. We argue that such systems offer significant potential for advancing both science and engineering. For science, evolving robots can be used to investigate fundamental issues about evolution and the emergence of embodied intelligence. For engineering, artificial evolution can be used as a tool that produces good designs in difficult applications in complex unstructured environments with (partially) unknown and possibly changing conditions. This implies a new paradigm, second-order software engineering, where instead of directly developing a system for a given application, we develop an evolutionary system that will develop the target system for us. Importantly, this also holds for the hardware; with a complete evolutionary robot system, both the software and the hardware are evolved. In this chapter, we discuss the long-term vision, elaborate on the main challenges, and present the initial results of an ongoing research project concerned with the first tangible implementation of such a robot system.

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Section: Discussionmentioning

confidence: 99%

Towards Autonomous Robot Evolution

Eiben

Hart

Timmis

et al. 2020

Software Engineering for Robotics

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“…For instance, in accordance with complex adaptive system theory, the fittest (multi-agent) systems, algorithms, software, and applications endure, pass on their architectures, and populate our techno-ecosystem (i.e., fitness here being solely based on systems’ ability to perform, adapt, survive, and (be) replicate(d) in a given ecological niche; [ 153 , 206 , 225 ]). In evolutionary robotics, the principles of variation and selective retention of traits are used by software engineers [ 84 , 152 ]. For instance, a first generation of codes—or “genotypes”– is generated as a potential solution to a problem (i.e., initial variations).…”

Section: Human-erobot Interaction and Co-evolution Modelmentioning

confidence: 99%

“…The robots’ fitness is then assessed in an environment, meaning that their code is translated into traits—or “phenotypes”—and their performance is observed to establish how well they interact with said environment to achieve goals. The fitness value determined by those observations then serves as a guide to select which robots will be used to seed the following generations; a process which is repeated until the targeted problem is solved [ 84 , 152 ]. Notably, these principles are now also being used to discover more efficient ML algorithms which could, in turn, enable artificial agents to learn and adapt more efficiently to uncertain environments and situations (e.g., human–machine (erotic) interaction) [ 246 ].…”

Section: Human-erobot Interaction and Co-evolution Modelmentioning

confidence: 99%

Foundations of Erobotics

Dubé

Anctil

2020

Int J of Soc Robotics

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Technology is giving rise to artificial erotic agents, which we call erobots ( erôs + bot). Erobots, such as virtual or augmented partners, erotic chatbots, and sex robots, increasingly expose humans to the possibility of intimacy and sexuality with artificial agents. Their advent has sparked academic and public debates: some denounce their risks (e.g., promotion of harmful sociosexual norms), while others defend their potential benefits (e.g., health, education, and research applications). Yet, the scientific study of human–machine erotic interaction is limited; no comprehensive theoretical models have been proposed and the empirical literature remains scarce. The current research programs investigating erotic technologies tend to focus on the risks and benefits of erobots, rather than providing solutions to resolve the former and enhance the latter. Moreover, we feel that these programs underestimate how humans and machines unpredictably interact and co-evolve, as well as the influence of sociocultural processes on technological development and meaning attribution. To comprehensively explore human–machine erotic interaction and co-evolution, we argue that we need a new unified transdisciplinary field of research—grounded in sexuality and technology positive frameworks—focusing on human-erobot interaction and co-evolution as well as guiding the development of beneficial erotic machines. We call this field Erobotics . As a first contribution to this new discipline, this article defines Erobotics and its related concepts; proposes a model of human-erobot interaction and co-evolution; and suggests a path to design beneficial erotic machines that could mitigate risks and enhance human well-being.

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“…It has been claimed within evolutionary robotics that "evolutionary methods provide a successful approach to designing robots" (Jelisavcic et al, 2019). However, so far the technique is little used by those who design robots for purposes other than researching evolutionary robotics.…”

Section: Motivation For Biological Realismmentioning

confidence: 99%

New Biological Morphogenetic Methods for Evolutionary Design of Robot Bodies

Hockings

Howard

2020

Front. Bioeng. Biotechnol.

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We present some currently unused morphogenetic mechanisms from evolutionary biology and guidelines for transfer to evolutionary robotics. (1) DNA patterns providing mutation of mutability, lead to canalization of evolvable bauplans, via kin selection. (2) Morphogenetic mechanisms (i) Epigenetic cell lines provide functional cell types, and identification of cell descent. (ii) Local anatomical coordinates based on diffusion of morphogens, facilitate evolvable genetic parameterizations of complex phenotypes (iii) Remodeling in response to mechanical forces facilitates robust production of well-integrated phenotypes of greater complexity than the genome. An approach is proposed for the tractable application of mutation-of-mutability and morphogenetic mechanisms in evolutionary robotics. The purpose of these methods, is to facilitate production of robot mechanisms of the subtlety, efficiency, and efficacy of the musculoskeletal and dermal systems of animals.

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Lamarckian Evolution of Simulated Modular Robots

Cited by 27 publications

References 60 publications

Towards Autonomous Robot Evolution

Towards Autonomous Robot Evolution

Foundations of Erobotics

New Biological Morphogenetic Methods for Evolutionary Design of Robot Bodies

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