基于神经元自动机的软体机器人再生
Regenerating Soft Robots through Neural Cellular Automata
摘要：
形态再生是反映生物系统环境适应能力的重要特征。缺乏这种再生能力会严重限制机器的恢复能力及其运行环境。为了帮助解决这一差距，我们开发了一种方法，模拟软体机器人再生的部分形态时，被损坏。虽然使用软体机器人的数值模拟在其设计中发挥了重要作用，但具有再生能力的软体机器人的发展迄今为止几乎没有受到关注。在这里，我们提出了一个软体机器人，通过神经元自动机再生模型。重要的是，这种方法只依赖于局部细胞信息来再生受损的组件，为未来物理可再生的软体机器人提供了有趣的可能性。我们的方法允许模拟受损的软体机器人通过局部细胞相互作用部分再生其原始形态，并恢复其部分迁移能力。这些结果朝着为人工系统配备再生能力迈出了一步，并可能允许在各种情况和环境中进行更强大的操作。本文中的实验代码可在以下网址获得：\url{github.com/KazuyaHoribe/RegeneratingSoftRobots}。
英文摘要：
Morphological regeneration is an important feature that highlights the environmental adaptive capacity of biological systems. Lack of this regenerative capacity significantly limits the resilience of machines and the environments they can operate in. To aid in addressing this gap, we develop an approach for simulated soft robots to regrow parts of their morphology when being damaged. Although numerical simulations using soft robots have played an important role in their design, evolving soft robots with regenerative capabilities have so far received comparable little attention. Here we propose a model for soft robots that regenerate through a neural cellular automata. Importantly, this approach only relies on local cell information to regrow damaged components, opening interesting possibilities for physical regenerable soft robots in the future. Our approach allows simulated soft robots that are damaged to partially regenerate their original morphology through local cell interactions alone and regain some of their ability to locomote. These results take a step towards equipping artificial systems with regenerative capacities and could potentially allow for more robust operations in a variety of situations and environments. The code for the experiments in this paper is available at: \url{github.com/KazuyaHoribe/RegeneratingSoftRobots}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
