论文标题
在可发展的机器人中学习运动技能
Learning Locomotion Skills in Evolvable Robots
论文作者
论文摘要
机器人繁殖的挑战 - 通过重新组合两个现有的机器人制造新机器人 - 最近已经破解了机器人系统的挑战。在这里，我们解决了下一个大障碍：为新生机器人产生足够的大脑。特别是，我们解决了有针对性的运动的任务，这可以说是任何实际实施中的基本技能。我们介绍了控制器体系结构和一种通用学习方法，以允许具有任意形状的模块化机器人，以学习朝目标行走，并在该目标移动时遵循该目标。在三个现实世界中，我们的方法在三个机器人，一个蜘蛛，壁虎及其后代进行了验证。
The challenge of robotic reproduction -- making of new robots by recombining two existing ones -- has been recently cracked and physically evolving robot systems have come within reach. Here we address the next big hurdle: producing an adequate brain for a newborn robot. In particular, we address the task of targeted locomotion which is arguably a fundamental skill in any practical implementation. We introduce a controller architecture and a generic learning method to allow a modular robot with an arbitrary shape to learn to walk towards a target and follow this target if it moves. Our approach is validated on three robots, a spider, a gecko, and their offspring, in three real-world scenarios.
