RI: Small: High Confidence, Efficient Learning Under Rich Task Specifications

RI：小：丰富任务规格下的高置信度、高效学习

基本信息

批准号：
1617639
负责人：
Scott Niekum
金额：
$ 47万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-08-01 至 2020-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617639&HistoricalAwards=false
关键词：
RI Small Confidence Efficient Learning

项目摘要

Artificially intelligent machines such as autonomous vehicles and personal robots are poised to contribute in many economic sectors, but cannot be deployed on a large scale without measurable confidence that they will operate correctly. This is especially true for safety-critical systems in which humans could be injured or infrastructure could be damaged by incorrect behavior. The proposed research will address this key issue by developing methods that allow intelligent agents to learn to perform challenging tasks and adapt to new situations, while simultaneously providing strong guarantees of correctness and safety. Once deployed, these future robotic systems will have broad impacts on society ranging from automating small manufacturing to giving new freedom to disabled and elderly populations through safe and personalized in-home care. The proposed robotics applications will additionally create opportunities for interactive educational K-12 programs to encourage interest in STEM areas, as well as undergraduate and graduate education.Towards these goals, the proposed work focuses on three primary research thrusts: 1) We will design safe learning algorithms that provide theoretical probabilistic satisfaction and data efficiency guarantees over both the expected reward of a policy and its correctness with respect to a high-level specification. 2) In order to account for the gap between theoretical and practical efficiency in learning, we will develop model-based and model-free off-policy evaluation methods that leverage active sampling strategies and bootstrapping to achieve practical efficiency. 3) We will develop hybrid techniques that combine and amplify the advantages of both strong theoretical and efficient practical guarantees. The merit of the proposed algorithms will be systematically evaluated on complex, real-world problems in robotic reconfigurable manufacturing that require learning optimized, yet safe policies with a high degree of confidence in settings with low-to-medium quantities of available data.

人工智能的机器（例如自动驾驶汽车和个人机器人）准备在许多经济领域做出贡献，但不能大规模部署，而没有可衡量的信心可以正确运行。对于可能受伤或基础设施可能会因不正确的行为而受伤的安全系统尤其如此。拟议的研究将通过开发允许智能代理人学习执行具有挑战性的任务并适应新情况的方法来解决这一关键问题，同时提供了强大的正确性和安全性的保证。部署后，这些未来的机器人系统将对社会产生广泛的影响，从自动化小型制造业到通过安全和个性化的家庭护理为残疾人和老年人提供新的自由。拟议的机器人技术应用程序还将为互动式教育K-12计划创造机会，以鼓励对STEM领域以及本科和研究生教育的兴趣。在这些目标上，拟议的工作重点介绍了三个主要的研究推力：1）我们将设计安全的学习算法，以提供对概率的满意度和数据的确定性，以确保对较高的奖励，并确保对一项正确的奖励，并确保其适当的奖励，并适用于一项正确的奖励。 2）为了说明学习理论和实践效率之间的差距，我们将开发基于模型和无模型的外部评估方法，以利用主动采样策略和引导程序来实现实践效率。 3）我们将开发混合技术，以结合和扩大强大的理论和有效实际保证的优势。在机器人重新配置的制造中，将对所提出的算法的优点进行系统的评估，这些制造需要学习优化，但对低到中等数量的可用数据的设置具有高度信心。