摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的最新研究结果已经发表。根据来自荷兰阿姆斯特丹的Newsrx记者的新闻报道，研究表明：“在机器人学中，在复杂环境中实现适应性是一项挑战。传统的机器人系统使用刚性材料和计算成本高昂的集中控制器，而大自然往往偏爱软材料和具身智能。”这项研究的资助者包括Horizon 2020，欧盟(EU)，荷兰科学研究组织(NWO)。我们的新闻记者从AMOLF的研究中获得了一句话：“受自然分布式智能的启发，本研究探索了一种分散的方法，在不了解软机器人系统的形状或环境的情况下，在软机器人系统中实现的一些基本规则可以使全身的光出租车在表面上朝着光源导航，”结果表明,该方法在动态环境和挑战性环境中具有良好的鲁棒性和自适应行为生成能力,并且该方法简单易行,能够直观地描述和理解所观察到的行为的内在机理,特别注意装配系统的几何形状和学习参数的影响,从而为进一步研究装配系统的性能提供了新的思路。这些发现为以最小的计算能力开发自适应的自主机器人系统提供了见解，为软和微型机器人以及在现实环境中运行的机器人的稳健和有用行为铺平了道路。本文阐述了软模块系统S如何在没有集中控制的情况下实现稳健的趋光行为，并阐明了模块间通信，但只能利用基本的局部规则。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Robotics have been published. According to news originating from Amsterdam, Netherlands, by N ewsRx correspondents, research stated, "In robotics, achieving adaptivity in com plex environments is challenging. Traditional robotic systems use stiff material s and computationally expensive centralized controllers, while nature often favo rs soft materials and embodied intelligence." Funders for this research include Horizon 2020, European Union (EU), Netherlands Organization for Scientific Research (NWO). Our news journalists obtained a quote from the research from AMOLF, "Inspired by nature's distributed intelligence, this study explores a decentralized approach for robust behavior in soft robotic systems without knowledge of their shape or environment. It is demonstrated that only a few basic rules implemented in iden tical modules that shape the soft robotic system can enable whole-body phototaxi s, navigating on a surface toward a light source, without explicit communication between modules or prior system knowledge. The results reveal the method's effe ctiveness in generating robust and adaptive behavior in dynamic and challenging environments. Moreover, the approach's simplicity makes it possible to illustrat e and understand the underlying mechanism of the observed behavior, paying parti cular attention to the geometry of the assembled system and the effect of learni ng parameters. Consequently, the findings offer insights into the development of adaptive, autonomous robotic systems with minimal computational power, paving t he way for robust and useful behavior in soft and microscale robots, as well as robotic matter, that operate in real-world environments. How soft modular system s can achieve robust phototactic behavior without centralized control and explic it intermodule communication, but only by leveraging basic local rules is explor ed."
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