Mergeable nervous systems for robots

Mathews, Nithin; Christensen, Anders Lyhne; O’Grady, Rehan; Mondada, Francesco; Dorigo, Marco

doi:10.1038/s41467-017-00109-2

Cited by 64 publications

(75 citation statements)

References 18 publications

(19 reference statements)

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“…Arbib & Fellous 2004;Asada 2015;Bostrom 2014;Briegel 2012;Cardon 2006;Clark 2015; in press a;Davies 2016;Fung 2015;Han et al 2013;Kaipa et al 2010;Lake et al 2018;Lumaca & Baggio 2017;Mathews et al 2017;McShea 2013;Parisi 1997;Thomaz & Cakmak 2013;Wallach et al 2010). Trends in state-of-the-art digital life research and development emphasize dynamic machine-learning construction, operation, and evolution of whole semi-autonomous artificial organisms, such as OpenWorm's simulated nematode Caenorhabditis elegans and the plant-science community's crop-on-a-chip Arabidopsis thaliana.…”

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confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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The processes underwriting the acquisition of culture remain unclear. How are shared habits, norms, and expectations learned and maintained with precision and reliability across large-scale sociocultural ensembles? Is there a unifying account of the mechanisms involved in the acquisition of culture? Notions such as “shared expectations,” the “selective patterning of attention and behaviour,” “cultural evolution,” “cultural inheritance,” and “implicit learning” are the main candidates to underpin a unifying account of cognition and the acquisition of culture; however, their interactions require greater specification and clarification. In this article, we integrate these candidates using the variational (free-energy) approach to human cognition and culture in theoretical neuroscience. We describe the construction by humans of social niches that afford epistemic resources called cultural affordances. We argue that human agents learn the shared habits, norms, and expectations of their culture through immersive participation in patterned cultural practices that selectively pattern attention and behaviour. We call this process “thinking through other minds” (TTOM) – in effect, the process of inferring other agents’ expectations about the world and how to behave in social context. We argue that for humans, information from and about other people's expectations constitutes the primary domain of statistical regularities that humans leverage to predict and organize behaviour. The integrative model we offer has implications that can advance theories of cognition, enculturation, adaptation, and psychopathology. Crucially, this formal (variational) treatment seeks to resolve key debates in current cognitive science, such as the distinction between internalist and externalist accounts of theory of mind abilities and the more fundamental distinction between dynamical and representational accounts of enactivism.

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confidence: 99%

Thinking through other minds: A variational approach to cognition and culture

et al. 2019

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“…In swarm robotics research so far, a form of morphological plasticity is possible through self-assembly into connected groups of various forms (Brambilla et al, 2013). Examples of this include the "s-bot" which can physically attach to each other (Mondada et al, 2004), conceptual demonstrations in "Kilobots" (Rubenstein et al, 2014;Slavkov et al, 2018;Carrillo-Zapata et al, 2019), or the idea of a "mergeable nervous system" (Mathews et al, 2017). More broadly, one can design robots to adapt their own morphology (Divband Soorati et al, 2019;Hauser, 2019;Kriegman et al, 2019); in combination such "multi-robot organisms" (Levi and Kernbach, 2010) may selforganize a wide range of adaptations.…”

Section: Physiological and Morphological Plasticitymentioning

confidence: 99%

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

Hunt

2020

Front. Robot. AI

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The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level "emergence" of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the "swarm philosophy" of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate-indicate the need for-plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity "for free" based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.

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“…而自重组机器人则相对于可重构机器人具有更强的自主性能够根据任务需求主动完成自身的重构 [231] . [255] 等相关技术也将会是未来软体机器人模块化方面的重要研究内容 [246] . [7] .…”

Section: 机器人各模块之间具有更强的通用性和兼容性并且unclassified