Editorial: Evolving Robotic Morphologies

Morphological adaptation is a key challenge for Evolutionary Robotics (Bongard, 2013;Doncieux et al., 2015), and robotics in general.Consideration of a robot's physical form in addition to its control system holds particular promise for imbuing robotic systems with task and environmental adaptability (Sims, 1994;Lipson and Pollack, 2000).The additional degrees of behavioural freedom that can be unlocked when morphology is considered in the evolutionary process allows access to a wide design space, providing a pathway towards resilient, rugged, and adaptable robots that can cope with unforeseen circumstances and be realistically deployed in real-world scenarios (Nygaard et al., 2021b), with corresponding real-world impacts.Morphology evolution allows us to create and study systems based on the principles of embodied cognition, whereby coupling (evolved) controllers to (evolved) physical substrates in a meaningful environment (Miras and Eiben, 2019) allows for surprising, rich behaviours that emerge from interactions between the three (Lehman and Stanley, 2011;Lehman et al., 2020).Embodied cognition provides a route towards improving the complexity and adaptivity of evolved behaviours, leading to a corresponding increase in the applicability of evolutionary robotics systems to challenging scenarios.Systems that can fully exploit the principles of embodied cognition and automatically evolve environment-adapted and task-specific robots is an ambitious and challenging endeavour.To date, evolutionary robotics research is largely conducted on morphologically static robots, mainly due to difficulties in either developing encodings that can effectively navigate the expanded, coupled search space of morphology-plus-controller (Faíña et al., 2013;Cheney et al., 2016), and fabricating/ assessing morphologically adaptable robotic hardware.A recent trend has seen implementations of morphological evolutionary robotics for challenging design domains, which has been shown to be effective in designing robots that are, for example, multi-material, soft (Cheney et al., 2014;Fitzgerald et al., 2021), or physically-embodied (Nygaard et al., 2021a).Emerging technologies offer the opportunity to push the boundaries of possibility for morphology evolution, and numerous recent examples from the literature harness principles including multi-material 3D printing (Howard et al., 2021) and advanced modular systems, alongside frameworks (Eiben and Smith, 2015;Howard et al., 2019) that allow researchers to operate in this rapidly-developing scientific field.This Research Topic called for contributions that illustrate and discuss innovative methods, technologies, software, and philosophical viewpoints that advance the field of evolving robotic morphologies.In the end, the topic accepted 7 original research articles and one mini-review, for a total of 8 accepted articles involving seventeen authors.In Nordmoen et al. , Nordmoen, Veenstra, Ellefsen and Glette compare three different evolutionary algorithms on the challenging task of optimizing morphology-controller pairings for modular robotics.They show that the quality-diversity algorithm MAP-Elites (Mouret and Clune, 2015) finds the highest-

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