CAREER: Understanding the role of sensory feedback in gait stability through neurorobotic modeling

职业：通过神经机器人建模了解感觉反馈在步态稳定性中的作用

• 批准号: 1943483
• 负责人: Alexander Hunt
• 金额: $ 52.5万
• 依托单位: Portland State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1943483&HistoricalAwards=false
• 关键词: CAREER; Understanding; role; sensory; feedback

This project aims to construct a comprehensive model of locomotion that unifies current theories, and to provide a more complete picture of how the neural system, body, and external environment interact. The application of neuroscience to robotics lags capabilities and knowledge in each field; this work remedies this by unifying the state-of-the-art in both fields. Robotics will be advanced through the development of neural controllers that copy the nature of what makes animal locomotion robust and adaptive. Neuroscience will be advanced through the embodiment of neural models on a physical system. These advances will lead to improved treatment and rehabilitation methods for disorders that affect locomotion, and robotic systems that can be used in unstructured environments and assistive contexts.This project seeks to build the foundation of neural based control systems for use in quadrupedal legged robots by identifying the minimal neural circuits required for stable locomotion. Additionally, this work will provide insights into how these circuits provide robust resistance to internal and external perturbations by investigating the interplay between mechanics, sensory feedback, and spinal circuits. The approach for this research is to encapsulate the latest neuroscience data of locomotion circuits into comprehensive neural models that run on real-time computing platforms, use them to control a biomimetic robot, and compare experimental results with neural and biomechanical animal data. The specific goal is to test computational neuroscience models of dynamic gait regulation and gait transitions, and evaluate their ability to maintain stable locomotion in a robot and explain animal behavior. Towards this goal, the objectives for this project are to: 1) Quantify the impact of full-body mechanics on understanding neural control by characterizing the differences between a neurorobot, neuromechanical simulations, and pure neural models, 2) Establish how sensory feedback and spinal circuits effect stability in response to different perturbations, and 3) Discover what neural mechanisms are necessary to maintain stability and guarantee successful gait transitions.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.

该项目旨在构建一个统一当前理论的综合运动模型，并提供神经系统，身体和外部环境如何相互作用的更完整的图片。神经科学在机器人技术中的应用落后于每个领域的能力和知识;这项工作通过统一这两个领域的最新技术来弥补这一点。机器人技术将通过开发神经控制器来推进，这些神经控制器复制了使动物运动鲁棒性和适应性的本质。神经科学将通过在物理系统上体现神经模型来推进。这些进展将导致改善的治疗和康复方法的障碍，影响运动，和机器人系统，可用于非结构化环境和辅助context.This项目旨在建立基于神经的控制系统的基础，用于四足机器人通过确定所需的稳定运动的最小神经回路。此外，这项工作将提供深入了解这些电路如何通过调查力学，感觉反馈和脊髓电路之间的相互作用提供强大的抵抗内部和外部扰动。这项研究的方法是将运动回路的最新神经科学数据封装到在实时计算平台上运行的综合神经模型中，用它们来控制仿生机器人，并将实验结果与神经和生物力学动物数据进行比较。具体目标是测试动态步态调节和步态转换的计算神经科学模型，并评估它们在机器人中保持稳定运动和解释动物行为的能力。为实现这一目标，该项目的目标是：1）通过表征神经机器人、神经机械模拟和纯神经模型之间的差异来量化全身力学对理解神经控制的影响，2）建立感觉反馈和脊髓回路如何响应不同的扰动来影响稳定性，和3）发现什么样的神经机制是必要的，以保持稳定性和保证成功的步态过渡。这个奖项反映了NSF的法定使命，并已被认为是值得支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

A Functional Subnetwork Approach to~Multistate Central Pattern Generator Phase Difference Control

多状态中央模式发生器相位差控制的功能子网方法

• 发表时间: 2022
• 期刊: Lecture notes in computer science
• 作者: Scharzenberger, C.;Hunt, A.
• 通讯作者: Hunt, A.

A Comparison of Absolute and Relative Neural Encoding Schemes in Addition and Subtraction Functional Subnetworks

加法和减法功能子网络中绝对和相对神经编码方案的比较

• 发表时间: 2023
• 期刊: Biomimetic and Biohybrid Systems. Living Machines 2023. Lecture Notes in Computer Science(
• 作者: Scharzenberger, C.

Biarticular Muscles Improve the Stability of a Neuromechanical Model of the Rat Hindlimb

双关节肌肉提高大鼠后肢神经力学模型的稳定性

• 发表时间: 2023
• 期刊: Biomimetic and Biohybrid Systems. Living Machines 2023. Lecture Notes in Computer Science(
• 作者: Deng, K.;Hunt, A.;Chiel, H.;Quinn, R.
• 通讯作者: Quinn, R.