Legged robots have significant potential to operate in highly unstructured environments. The design of locomotion control is, however, still challenging. Currently, controllers must be either manually designed for specific robots and tasks, or automatically designed via machine learning methods that require long training times and yield large opaque controllers. Drawing inspiration from animal locomotion, we propose a simple yet versatile modular neural control structure with fast learning. The key advantages of our approach are that behavior-specific control modules can be added incrementally to obtain increasingly complex emergent locomotion behaviors, and that neural connections interfacing with existing modules can be quickly and automatically learned. In a series of experiments, we show how eight modules can be quickly learned and added to a base control module to obtain emergent adaptive behaviors allowing a hexapod robot to navigate in complex environments. We also show that modules can be added and removed during operation without affecting the functionality of the remaining controller. Finally, the control approach was successfully demonstrated on a physical hexapod robot. Taken together, our study reveals a significant step towards fast automatic design of versatile neural locomotion control for complex robotic systems.
翻译:悬浮机器人在高度结构化环境中操作的潜力很大。 然而, 移动控制的设计仍然具有挑战性。 目前, 控制器必须或者为特定的机器人和任务手工设计, 或者通过机器学习方法自动设计, 需要很长的培训时间, 并产生大量不透明的控制器。 我们从动物移动中获取灵感, 我们建议一个简单而多功能的模块神经控制结构, 并快速学习。 我们的方法的主要优点是, 行为特定控制模块可以逐步添加, 以获得日益复杂的突发移动行为, 并且与现有模块的神经连接可以快速和自动地学习。 在一系列实验中, 我们展示了八个模块是如何快速学习的, 并添加到一个基础控制模块中, 以便获得一个允许六肢机器人在复杂环境中航行的随机适应行为。 我们还表明, 在操作过程中可以添加和删除模块, 而不会影响剩余控制器的功能。 最后, 控制方法在一个物理的六肢机器人上得到了成功演示。 一起, 我们的研究显示, 我们的研究表明, 朝着快速自动设计复杂机器人的多功能移动控制器的快速设计迈出了一大步步步。