简单神经回路的自组织适应使复杂的机器人行为成为可能
Self-organized adaptation of a simple neural circuit enables complex robot behaviour
摘要：
控制高等动物或复杂机器人的感觉运动系统是一个具有挑战性的组合问题，因为许多感觉信号需要同时协调成一个广泛的行为谱。为了与环境快速交互，这种控制需要快速和自适应。目前的机器人解决方案具有有限的自主性，并且大多限于少数行为模式。在这里，我们引入混沌控制作为一种新的策略，以产生复杂的行为的自主机器人。在所提出的系统中，18个传感器通过简单的神经控制电路驱动18个电机，从而生成11个基本行为模式（例如，定向、滑行、自我保护、各种步态）及其组合。控制信号快速且可逆地适应新的情况，并且还能够学习和突触长期存储行为上有用的运动反应。因此，这种神经控制提供了一种强大而简单的方法，可以在具有许多自由度的自主代理中自组织多功能行为。
英文摘要：
Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Current robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors via a simple neural control circuit, thereby generating 11 basic behavioural patterns (e.g., orienting, taxis, self-protection, various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and additionally enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.
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。
