Biological systems are very robust to morphological damage, but artificial systems (robots) are currently not. In this paper we present a system based on neural cellular automata, in which locomoting robots are evolved and then given the ability to regenerate their morphology from damage through gradient-based training. Our approach thus combines the benefits of evolution to discover a wide range of different robot morphologies, with the efficiency of supervised training for robustness through differentiable update rules. The resulting neural cellular automata are able to grow virtual robots capable of regaining more than 80% of their functionality, even after severe types of morphological damage.

生物系统对形态损伤非常稳健，但人工系统（机器人）目前不是。在本文中，我们提出了一个基于神经元胞自动机的系统，其中运动机器人得到进化，然后通过基于梯度的训练赋予其从损伤中再生其形态的能力。因此，我们的方法结合了进化的好处，以发现各种不同的机器人形态，以及通过可微更新规则进行鲁棒性监督训练的效率。由此产生的神经元胞自动机能够培育出能够恢复其 80% 以上功能的虚拟机器人，即使在严重的形态损伤后也是如此。