NCS-FO: Collaborative Research: Integrative Foundations for Interactions of Complex Neural and Neuro-inspired Systems with Realistic Environments

该项目将深度学习网络的能力集成到一个受生物启发的传感器电机控制体系结构中，该体系结构可用于为复杂的工程系统设计更强大的平台。对感觉运动规划和控制的灵活性和背景方面的研究将扩展现有的人-机器人交互范例，并作为创造能够在自然环境中操作的个人助理的基础。随着对这些分层体系结构在大脑中如何组织以产生高度健壮、灵活和高效行为的理解的加深，将在复杂环境中的快速发展技术中有许多应用，包括物联网、自主运输和可持续能源网络。神经系统是一个分层体系结构，以本项目旨在更深入地理解和模仿先进技术的方式，无缝地将高层规划与快速的底层感知、反射和行动相结合。中心目标是为分层体系结构开发一个同时考虑系统级功能需求和硬件约束的理论框架。在使用分层结构进行主动反馈控制方面，生物学和技术既有显著的共性，也有显著的差异。最突出和普遍的硬件约束是速度、精度和成本(构建、操作和维护)之间的权衡，成功的体系结构巧妙地结合不同的组件来创建既快速又准确的系统，尽管这些系统是由不是由其他部件构建的。最近的进展使得使用鲁棒控制中的最坏情况L无穷大有界不确定性模型以连贯和严格的方式整合感觉运动协调的现实特征和约束成为可能，但要实现其在神经科学和神经启发工程中的潜力，仍有许多工作要探索。该框架的另一个应用将是软件定义的联网，它明确地将数据转发和数据控制分开，并提供一个接口，通过该接口，网络应用(例如流量工程、拥塞控制和缓存)可以编程地控制网络。这使得它成为整合规划/反射分层理论的潜在“杀手级应用”；研究合作者将渴望在试验台上和规模上部署新的协议。

This project will integrate the capabilities of deep learning networks into a biologically inspired architecture for sensorimotor control that can be used to design more robust platforms for complex engineered systems. Studies of the flexibility and contextual aspects of sensorimotor planning and control will extend existing paradigms for human-robot interactions and serve as the foundation for creating personal assistants that are able to operate in natural settings. Growing understanding of how these layered architectures are organized in the brain to produce highly robust, flexible, and efficient behavior will have many applications to rapidly evolving technologies in complex environments, including the Internet of Things, autonomous transportation, and sustainable energy networks.The nervous system is a layered architecture, seamlessly integrating high-level planning with fast lower level sensing, reflex, and action in a way that this project aims to both understand more deeply and mimic in advanced technology. The central goal is to develop a theoretical framework for layered architectures that takes into account both system level functional requirements and hardware constraints. There are both striking commonalities and significant differences between biology and technology in using layered architectures for active feedback control. The most salient and universal hardware constraints are tradeoffs between speed, accuracy, and costs (to build, operate, and maintain), and successful architectures cleverly combine diverse components to create systems that are both fast and accurate, despite being built from parts that are not. Recent progress has made it possible to integrate realistic features and constraints for sensorimotor coordination in a coherent and rigorous way using worst-case L-infinity bounded uncertainty models from robust control, but much remains to explore to realize its potential in neuroscience and neuro-inspired engineering. Another application of this framework will be to software defined networking, which explicitly separates data forwarding and data control, and provides an interface through which network applications (such as traffic engineering, congestion control and caching) can programmatically control the network. This makes it a potential "killer app" for a theory of integrated planning/reflex layering; research collaborators will be eager to deploy new protocols on testbeds and at scale.