CAREER: A Skill-Driven Cooperative Learning Framework for Cyber-Physical Autonomy

职业：技能驱动的网络物理自主合作学习框架

基本信息

批准号：
2047010
负责人：
Xiangnan Zhong
金额：
$ 50.36万
依托单位：
Florida Atlantic University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2047010&HistoricalAwards=false
关键词：
CAREER Skill Driven Cooperative Learning

项目摘要

This project investigates new reinforcement learning (RL) approaches for cyber-physical autonomy to bridge the gap between current intelligent systems and human-level intelligence. The nature of many cyber-physical systems (CPS) is distributed, heterogeneous, and high-dimensional, making the hand-coded functions and task-specific information hard to design in the learning scheme. Large amount of training data is often required for achieving the desired performance, however this limits the generalization to other tasks. Hence, this project is to explore the new RL strategies to enable CPS with the capabilities of autonomous learning and generalization to rapidly adapt in unknown situations that were not assumed in the design phase. The results are expected to transform how agents interact in high-dimensional and heterogeneous environment, and therefore could potentially provide in-depth findings for exploring creativity in frontier Artificial Intelligence techniques. The goal of this project is to advance foundational knowledge and scientific methodologies of reinforcement learning for generalization and scalability in CPS. Motivated by the recent research in neurobiology and psychology, this project will design a new skill-driven intelligent control approach for CPS that can learn more expressive extended skills to autonomously and adaptively handle unknown situations without further human intervention. The proposed approach will also develop cooperative learning strategies to share with extended skills to facilitate exploration and prevent agents from getting confused by the action details. In addition, this project will develop self-motivated learning structures to contribute towards the global objectives for team-wide success in a distributed perspective. The developed methods and associated architectures will provide critical insights and guidelines to foster autonomous learning and generalization in CPS. The integration of research and education plans will prepare the future workforce in the fields of CPS, artificial intelligence, learning and control. The outreach activities will build connections between the CPS research, and minority groups (women and Hispanic students), K-12, and college students through various learning approaches.This project is in response to the NSF CAREER 20-525 solicitation.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.

该项目研究了用于网络物理自主权的新的强化学习方法（RL）方法，以弥合当前智能系统与人类水平情报之间的差距。许多网络物理系统（CPS）的性质是分布，异质和高维的，这使得在学习方案中难以设计手工编码的功能和特定于任务的信息。实现所需的性能通常需要大量培训数据，但是这将概括限制在其他任务上。因此，该项目旨在探索新的RL策略，以使CP具有自主学习和概括的能力，以在设计阶段未假定的未知情况下快速适应。预计结果将改变代理在高维和异质环境中的相互作用，因此有可能提供深入的发现，以探索边境人工智能技术中的创造力。该项目的目的是推进CPS概括和可伸缩性的强化学习的基础知识和科学方法。在最近的神经生物学和心理学研究的激励下，该项目将为CPS设计一种新的技能驱动的智能控制方法，可以学习更具表现力的扩展技能，以自主和适应地处理未知情况，而无需进一步的人类干预。拟议的方法还将制定合作学习策略，以分享扩展的技能，以促进探索并防止代理人对动作细节感到困惑。此外，该项目将开发自我激励的学习结构，以在分布式观点中为全球范围的全球目标做出贡献。开发的方法和相关架构将提供关键的见解和指南，以促进CPS中的自主学习和泛化。研究和教育计划的整合将为CP，人工智能，学习和控制领域的未来劳动力做好准备。 The outreach activities will build connections between the CPS research, and minority groups (women and Hispanic students), K-12, and college students through various learning approaches.This project is in response to the NSF CAREER 20-525 solicitation.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.