FuSe: Bio-inspired sensorimotor control for robotic locomotion with neuromorphic architectures using beyond-CMOS materials and devices

FuSe：使用超越 CMOS 材料和设备的神经形态架构的机器人运动仿生感觉运动控制

• 批准号: 2328815
• 负责人: Rajkumar Chinnakonda Kubendran
• 金额: $ 160.65万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2328815&HistoricalAwards=false
• 关键词: FuSe; Bio; inspired; sensorimotor; control

In biology, a key property of mammals is the ability to move efficiently in complex environments which is enabled through neural networks called Central Pattern Generators (CPG). CPGs produce rhythmic patterns of control signals for the limbs using simple environmental cues. A prime example of CPGs in action is our own ability to navigate around obstacles. While such networks are natural from the perspective of mammals, there is currently no efficient way to engineer them using electronic devices and computers. Engineering these networks can revolutionize our ability to build future generations of robots. Agile robots that can traverse unknown and complex terrains have the potential to enable autonomous navigation for commercial transport, enhance disaster response during floods and earthquakes or to access remote and unsafe areas like malfunctioning nuclear plants or space exploration. The advances in computer engineering hardware including circuits, devices, and materials that form the core of this project will aid the creation of this new technology. The breadth of skillsets that are required to effectively train a new cadre of workforce in neuromorphic engineering for robotics makes curriculum design and integration with existing frameworks incredibly challenging. The proposed NeuRoBots educational consortium among the partnering institutions will address this issue. The main objective of this consortium is to collaborate and implement a comprehensive workforce development plan that incorporates evidence-based best practices to help train a new generation of engineers and researchers, who are equipped to satisfy the growing needs of the semiconductor industry. The goal of this award is to model, design and implement neuromorphic networks with synapses and neurons using emerging devices to achieve efficient and adaptive control in miniature robots. The inspiration comes from biological neural circuitry responsible for agile movement. The technical objectives of this project are designed to address three components: 1) The materials track focuses on physics-inspired models to understand the material and device properties to help engineer the temporal dynamics, 2) the devices track develops new devices and circuits for implementing bio-inspired neurons and synapses, and 3) the systems track will implement agile miniature robots that demonstrate bio-inspired locomotion using CPG networks and reinforcement learning. By incorporating non-linear temporal dynamics at multiple timescales through mixed-feedback control and instantiating CPG networks on scalable energy-efficient hardware built using novel devices, the target is to demonstrate a fully functional quadruped/hexapod robot that can learn to move using principles informed by neuroscience. This work can lead to transformative advances in neuromorphic computing, artificial intelligence (AI), robotics, and industrial automation, while providing deeper insights to the science of neuromodulation and self-supervised learning. Development of general-purpose neuromorphic systems that mimic the complex neuromodulatory temporal dynamics seen in neuroscience experiments offers pathways to build a new class of computing machines that address the grand challenges of the BRAIN Initiative, and advances envisioned in the CHIPS Act, benefiting the nation and society at large.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在生物学中，哺乳动物的一个关键特性是在复杂环境中有效移动的能力，这是通过称为中央模式生成器（CPG）的神经网络实现的。CPG使用简单的环境线索为肢体产生控制信号的节奏模式。CPG在行动中的一个主要例子是我们自己绕过障碍的能力。虽然从哺乳动物的角度来看，这样的网络是自然的，但目前还没有有效的方法使用电子设备和计算机来设计它们。设计这些网络可以彻底改变我们建造未来几代机器人的能力。能够穿越未知和复杂地形的敏捷机器人有可能实现商业运输的自主导航，增强洪水和地震期间的灾难响应，或进入偏远和不安全的地区，如发生故障的核电站或太空探索。计算机工程硬件的进步，包括构成该项目核心的电路、设备和材料，将有助于这项新技术的创造。有效培训机器人神经形态工程新干部所需的技能组合的广度使得课程设计和与现有框架的整合具有极大的挑战性。合作机构中拟议的NeuRoBots教育联盟将解决这一问题。该联盟的主要目标是合作并实施一项全面的劳动力发展计划，其中包括基于证据的最佳实践，以帮助培训新一代的工程师和研究人员，以满足半导体行业日益增长的需求。该奖项的目标是使用新兴设备建模，设计和实现具有突触和神经元的神经形态网络，以实现微型机器人的高效和自适应控制。灵感来自负责敏捷运动的生物神经回路。该项目的技术目标旨在解决三个组成部分：1）材料轨道侧重于物理启发的模型，以了解材料和设备属性，以帮助工程师的时间动态，2）设备轨道开发新的设备和电路，用于实现生物启发的神经元和突触，3）系统轨道将实现敏捷的微型机器人，使用CPG网络和强化学习展示生物启发的运动。通过混合反馈控制在多个时间尺度上结合非线性时间动态，并在使用新型设备构建的可扩展节能硬件上实例化CPG网络，目标是展示一个功能齐全的四足/六足机器人，该机器人可以使用神经科学原理学习移动。这项工作可以导致神经形态计算，人工智能（AI），机器人和工业自动化的变革性进步，同时为神经调节和自我监督学习科学提供更深入的见解。模拟神经科学实验中所见的复杂神经调节时间动力学的通用神经形态系统的开发，为构建一类新的计算机提供了途径，这些计算机可以解决大脑计划的巨大挑战，以及芯片法案中设想的进步，该奖项反映了NSF的法定使命，并通过使用基金会的学术价值和更广泛的影响审查标准。