A Neuromorphic Control System for Agile Biped Walking

伦敦主干线火车站的高峰时间：一名乘客轻松地快速穿过漩涡般的人群，看着20米外的大屏幕，左手拿着手机，右手端着一杯咖啡，避免与任何人相撞，向14号站台走去。这似乎毫不费力。但从机器人的角度来看，这几乎是不可思议的，因为所有这些任务都是由一个大脑有效地并行控制的。今天的机器人要想像人类一样工作，就必须使用一台价值百万美元的超级计算机或几台连接在一起的计算机，这些计算机使用千瓦级的能量，而其性能仍然无法与人类的大脑相比。通过对人类或动物大脑的逆向工程来构建机器人大脑是近年来许多大型跨学科项目的最终目标。今天，在物理和结构上模拟大脑最有前途的技术是神经形态工程，它使用电子电路来模拟神经生物结构。与标准的基于计算机的控制器相比，神经形态控制器自然是并行的，更紧凑，更节能。人们普遍认为，神经形态的大脑将是下一代智能自主机器人的中心。神经形态工程的许多研究已经开发出神经形态系统来实现大脑的特定功能模块，如听觉、视觉、嗅觉、认知和行动学习。这个计划的目标是人脑的另一个基本控制功能——两足行走。就像人类和动物一样，为了在自然环境中执行任务，机器人必须能够灵活地移动。但是，与传统机器人相比，神经形态控制的有腿机器人（特别是双足机器人）在多用途和敏捷运动方面的表现非常差。这主要是因为它们的神经形态回路只模拟了脊神经网络的基本功能模块，只能实现推进控制。在动物中，推进控制和身体姿态控制是完全结合在一起的，这是它们在复杂的自然环境中灵活运动的基础。特别是在人类中，为了满足敏捷双足行走的功能要求，脊柱神经网络受到椎骨上水平的严重调节。然而，在生物学上仍未完全了解脊髓水平和棘上水平的神经元模块如何相互作用和调节来控制人类两足运动。基于团队在两足机器人，神经形态电路设计，神经形态模拟和计算神经科学方面的记录，该项目旨在通过开发多模块和多层次（即脊柱水平和脊柱上水平）神经形态系统来填补这一空白。在本项目的神经形态系统中，我们将实现已知在人类运动控制中发挥重要作用的三个神经元模块的功能。通过使用一种新方法（模型驱动并发集成）将这种神经形态系统与专门设计的双足机器人耦合在一起，我们将能够探索这些模块之间未知的交互/调制机制，这些机制可能导致敏捷的双足行走。我们提案的核心目标是在神经形态机器人领域取得显著进展。这个项目将首次展示一个敏捷的3D双足机器人，它具有类似人类的行走模式和神经形态控制机制。

Rush-hour in a London mainline railway station: a passenger effortlessly walks swiftly through the swirling crowd, looking at a large screen 20 meters away, talking on a mobile phone in his left hand, holding a cup of coffee in his right hand, avoiding collision with anyone, and making his way to platform 14. This seems effortless. But from the robotics view, this is almost miraculous because all these tasks are controlled by a single brain, efficiently in parallel. To behave like this human, today's robot would have to use a large million-dollar super-computer or several connected computers using Kilowatts of energy, and the performance would still not comparable to that of a human brain. To build a robot brain by reverse engineering the human or animal brain has been the ultimate goal of many large inter-disciplinary projects in recent years. Today, the most promising technology to physically and structurally emulate the brain is neuromorphic engineering, which uses electronic circuits to mimic neuro-biological architectures. Compared with standard computer-based controllers, neuromorphic controllers are naturally parallel, more compact and more energy efficient. It is widely thought that a neuromorphic brain will be the centre of the next generation of intelligent autonomous robots. Many studies in neuromorphic engineering have developed neuromorphic systems to realize specific functional modules of the brain, e.g., hearing, vision, olfaction, cognition, and action learning. The proposed project is targeting another fundamental control function of the human brain -- bipedal (two-legged) walking. Just like humans and animals, a robot must be able to move agilely in order to execute its tasks in the natural environment. But, compared with traditional counterparts, the performance of the neuromorphically controlled legged robots (especially biped robots) is very poor in terms of versatile and agile locomotion. This is mainly because their neuromorphic circuits emulated only the basic function module of the spinal neural network, which could only realize propulsion control. In animals, propulsion control and body posture control are fully integrated, which is fundamental for their agile locomotion in a complex natural environment. Particularly, in humans, to meet the functional requirements of agile bipedal walking, the spinal neural network is heavily modulated by the supraspinal levels. However, it is still not fully understood in biology how the neuronal modules at the spinal level and supraspinal level interact with and modulate each other in the control of human bipedal locomotion. Building on the team's track record in biped robotics, neuromorphic circuit design, neuromorphic simulation, and computational neuroscience, the proposed project aims to fill this gap via developing a multi-module and multi-level (i.e., spinal level and supraspinal level) neuromorphic system. In the neuromorphic system in this project, we will implement the functions of three neuronal modules that have been known to play important roles in human locomotion control. By coupling such a neuromorphic system with a purposely designed biped robot using a new method (model-driven concurrent integration), we will be able to explore the unknown interaction/modulation mechanisms between these modules that could lead to agile biped walking.At the heart of our proposal is the ambition to make a notable step forward in the area of neuromorphic robotics. This project will, for the first time, demonstrate an agile 3D biped robot that has human-like walking patterns and a neuromorphic control mechanism.