late, Duarte et al. [@Duarte:2016:EER:2908812.2908855] have presented EvoRBC, an approach to evolve a control system for robots with arbitrary locomotion complexity and implemented it on a simulated robot. They have used a Quality Diversity algorithm to build a repertoire of behaviour primitives (for e.g. move straight, turn right). This repertoire is then used to evolve an ANN which maps the sensory inputs of the robot to an appropriate primitive.

In most of the above-reported approaches, the subcontrollers are evolved separately and lack the essence of continuous, distributed, decentralized and on-the-fly learning.

Embodied Evolution
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A recent comprehensive paper by Bredeche et al. [@bredeche2017embodied] presents a review of the research published since the inception of the term Embodied Evolution (1997-2017). EE involves continuous and online learning in a collective of robots. A population of robots learns in a decentralized manner by sharing the controllers evolved among the peer robots. Although there has been a recent surge in papers [@bredeche2017embodied] where EE has been successfully applied, most of the work that cites the use of real robots [@watson2002embodied; @heinerman2016line; @simoes2001embedding; @o2014distributed] are constrained to a single controller that can solve relatively simple tasks such as obstacle avoidance and phototaxis. Only recent, a 