CAREER: Safe and Efficient Robot Learning from Demonstration in the Real World

职业：安全高效的机器人从现实世界的演示中学习

基本信息

批准号：
1749204
负责人：
Scott Niekum
金额：
$ 52.46万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749204&HistoricalAwards=false
关键词：
CAREER Safe Efficient Robot Learning

项目摘要

General purpose robots are poised to enter the home and workplace in unprecedented numbers in coming years, but face the significant challenge of customization - the ability to perform user-specified tasks in many different unstructured environments. In response to this need, robot learning from demonstration (LfD) has emerged as a paradigm that allows users to quickly and naturally program robots by simply showing them how to perform a task, rather than by writing code. This methodology aims to allow non-expert users to program robots, as well as communicate embodied knowledge that is difficult to translate into formal code. However, current state-of-the-art LfD algorithms are not yet ready for widespread deployment, as they are often unreliable, need too much data, and are designed to learn in a single session in a laboratory setting. This work addresses these issues to help enable future robots to perform important tasks ranging from in-home elderly care to reconfigurable manufacturing.Specifically, this work identifies three significant technical improvements to current LfD algorithms that are needed before they can be deployed in the real world: the need for safety guarantees, the ability to learn from very limited amounts of data, and the ability to continually improve in an ongoing, life-long fashion. A formal theory of safe LfD is developed, along with practical algorithms that provide strong probabilistic lower bounds on agent performance. Algorithmic efficiency is addressed via a re-examining of common statistical assumptions (such as independent and identically distributed data) and through the use of multimodal side-information, such as natural language and gaze. Finally, active learning strategies and modeling of human beliefs are used to enable interactive, continual learning.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.

通用机器人有望在未来几年内以前所未有的数字进入家庭和工作场所，但是面临定制的重大挑战 - 在许多不同的非结构化环境中执行用户指定的任务的能力。为了应对这一需求，机器人从演示中学习（LFD）已成为一种范式，该范式允许用户通过简单地向他们展示如何执行任务而不是通过编写代码来快速自然地编程机器人。该方法旨在允许非专家用户编程机器人，并传达难以转化为正式代码的体现知识。但是，当前的最新LFD算法尚未准备好进行广泛的部署，因为它们通常不可靠，需要太多数据，并且旨在在实验室环境中进行一次学习。这项工作解决了这些问题，以帮助使未来的机器人执行重要任务，从家庭护理到可重新配置的制造业。具体来说，这项工作确定了当前的LFD算法的三个重大技术改进，需要将它们部署到现实世界中：从事安全的能力，以及在生活中的能力以及在生活中的能力以及在生活中的能力，并且在生活中的学习能力不断，并且在生活中的能力不断，并且在一定的能力中学习。制定了安全LFD的形式理论，以及实用算法，为代理性能提供了强大的概率下限。通过重新检查常见统计假设（例如独立和相同分布的数据）以及使用多模式侧信息，例如自然语言和凝视，可以解决算法效率。最后，积极的学习策略和人类信念的建模用于实现互动，持续学习。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来支持的。