Growing simulated robots with environmental feedback: an eco-evo-devo approach
Growing simulated robots with environmental feedback: an eco-evo-devo approach
复制标题
培育具有环境反馈的模拟机器人：生态进化发展方法
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Risi
中科院分区：
文献类型：
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作者：
Kathryn Walker;H. Hauser;S. Risi
机器人仍然缺乏适应新环境的能力。然而，生物系统能够轻松地适应新的环境;也许是因为它们有能力在生长阶段对环境输入做出反应，不仅在行为上，而且在形态上。然而，在机器人领域，基于环境的形态学发展是一个研究不足的领域。在本文中，我们使用进化算法来进化能够诱导机器人基于环境的发育可塑性的神经元胞自动机。我们使用每个细胞及其邻居的动能作为网络的输入，其输出决定了新细胞生长的位置。我们首先在三个单独的环境中进化我们的神经元胞自动机，然后在多个环境中进行性能评估。我们表明，使用环境反馈的网络优于那些不使用环境反馈的网络，并且通过在开发过程中引入环境反馈，更适应和性能更好的机器人是可能的。
Robots are still missing the ability to adapt to new environments. However, biological systems are able to adapt to new environments with ease; perhaps because they have the ability to react to environmental input during a growth phase with changes not only in behaviour, but also morphology. Yet within the field of robots, environmental based development of morphology is an under researched area. In this paper we use an evolutionary algorithm to evolve neural cellular automata capable of inducing environmental based developmental plasticity in robots. We use the kinetic energy of each cell and its neighbours as an input to our network, the output of which determines the position of new cell growth. We evolve our neural cellular automata first in three individual environments and then also for performance in multiple environments. We show that the networks that use environmental feedback outperform those that do not and that by introducing environmental feedback during development, more adaptive and better performing robots are potentially possible.