A Neuromorphic Control System for Agile Biped Walking

用于敏捷双足步行的神经形态控制系统

• 批准号: EP/P00542X/1
• 负责人: Tao Geng
• 金额: $ 58.16万
• 依托单位: Middlesex University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP00542X%2F1
• 关键词: Neuromorphic; Control; System; Agile; Biped

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.

伦敦干线火车站高峰时段：一名乘客毫不费力地快速穿过旋转的人群，看着20米外的大屏幕，左手拿着手机，右手拿着一杯咖啡，避开与任何人发生碰撞，然后到达 14 号站台。这看起来毫不费力。但从机器人学的角度来看，这几乎是奇迹，因为所有这些任务都是由一个大脑有效地并行控制的。为了像人类一样行动，今天的机器人必须使用一台价值数百万美元的大型超级计算机或几台使用千瓦能量的连接计算机，而且其性能仍然无法与人脑相媲美。通过对人类或动物大脑进行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。如今，在物理和结构上模拟大脑的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、更紧凑且更节能。人们普遍认为神经形态大脑将成为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，例如听觉、视觉、嗅觉、认知和动作学习。该项目的目标是人脑的另一种基本控制功能——双足行走。就像人类和动物一样，机器人必须能够灵活移动才能在自然环境中执行任务。但是，与传统机器人相比，神经形态控制的腿式机器人（尤其是双足机器人）在多功能和敏捷运动方面的性能非常差。这主要是因为它们的神经形态电路仅模拟了脊髓神经网络的基本功能模块，只能实现推进控制。在动物中，推进控制和身体姿势控制是完全集成的，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，脊髓神经网络受到脊髓上水平的强烈调节。然而，生物学上尚未完全理解脊髓水平和脊髓上水平的神经元模块如何在控制人类双足运动中相互作用和相互调节。基于该团队在双足机器人、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，拟议项目旨在通过开发多模块和多层次（即脊髓水平和脊髓上水平）神经形态系统来填补这一空白。在这个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。通过使用新方法（模型驱动并发集成）将这样的神经形态系统与专门设计的双足机器人耦合起来，我们将能够探索这些模块之间未知的交互/调节机制，从而实现敏捷的双足行走。我们的提案的目标是在神经形态机器人领域取得显着的进步。该项目将首次展示敏捷的 3D 双足机器人，该机器人具有类似人类的行走模式和神经形态控制机制。

项目成果

Introducing Rotary Force to a Template Model Can Explain Human Compliant Slope Walking

将旋转力引入模板模型可以解释符合人体工程学的斜坡行走

• 发表时间: 2017
• 作者: Wang X.

CABots and Other Neural Agents.

CABot 和其他神经代理。

• DOI: 10.3389/fnbot.2018.00079
• 发表时间: 2018
• 期刊: Frontiers in neurorobotics
• 影响因子: 3.1
• 作者: Huyck C

Fast Walking with Rhythmic Sway of Torso in A 2D Passive Ankle Walker

使用 2D 被动踝步行器快速行走并有节奏地摇摆躯干

• 发表时间: 2018
• 作者: Ruizhi Bao

Neuromorphic Building Blocks for Locomotion Pattern Generation

用于生成运动模式的神经形态构建模块

• DOI: 10.1109/mlcr57210.2022.00010
• 发表时间: 2022
• 作者: Gandhi V

Hot coffee: associative memory with bump attractor cell assemblies of spiking neurons.

热咖啡：与尖峰神经元的凹凸吸引子细胞组件相关的联想记忆。

• DOI: 10.1007/s10827-020-00758-1
• 发表时间: 2020
• 期刊: Journal of computational neuroscience
• 影响因子: 1.2
• 作者: Huyck CR