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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：
2323384
负责人：
Scott Niekum
金额：
$ 52.46万
依托单位：
University of Massachusetts Amherst
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323384&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.

未来几年，通用机器人将以前所未有的数量进入家庭和工作场所，但面临着定制的重大挑战--在许多不同的非结构化环境中执行用户指定任务的能力。为了满足这一需求，机器人从演示中学习（LfD）已经成为一种范式，允许用户通过简单地向机器人展示如何执行任务而不是编写代码来快速自然地对机器人进行编程。这种方法的目的是允许非专家用户编程机器人，以及沟通的具体知识，是很难转化为正式的代码。然而，目前最先进的LfD算法还没有准备好广泛部署，因为它们通常不可靠，需要太多的数据，并且被设计为在实验室环境中的单个会话中学习。这项工作解决了这些问题，以帮助未来的机器人能够执行从家庭老人护理到可重构制造等重要任务。具体来说，这项工作确定了当前LfD算法的三项重大技术改进，这些改进是在真实的世界中部署之前所需要的：对安全保障的需求，从非常有限的数据中学习的能力，以及以持续的终身方式不断改进的能力。一个正式的理论安全LfD开发，沿着与实用的算法，提供强大的概率下限代理性能。通过重新检查常见的统计假设（如独立和相同分布的数据），并通过使用多模态边信息，如自然语言和凝视，解决了数学效率。最后，积极的学习策略和人类信仰的建模被用来实现互动，持续的学习。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Ranking Game for Imitation Learning

DOI：
发表时间：
2022-02
期刊：
ArXiv
影响因子：
0
作者：
Harshit S. Sikchi;Akanksha Saran;Wonjoon Goo;S. Niekum
通讯作者：
Harshit S. Sikchi;Akanksha Saran;Wonjoon Goo;S. Niekum

Learning Optimal Advantage from Preferences and Mistaking it for Reward

DOI：
10.48550/arxiv.2310.02456
发表时间：
2023-10
期刊：
ArXiv
影响因子：
0
作者：
W. B. Knox;Stephane Hatgis-Kessell;Sigurdur O. Adalgeirsson;Serena Booth;Anca D. Dragan;Peter Stone;S. Niekum
通讯作者：
W. B. Knox;Stephane Hatgis-Kessell;Sigurdur O. Adalgeirsson;Serena Booth;Anca D. Dragan;Peter Stone;S. Niekum

Score Models for Offline Goal-Conditioned Reinforcement Learning

离线目标条件强化学习的评分模型

DOI：
发表时间：
2024
期刊：
International Conference on Learning Representations
影响因子：
0
作者：
Sikchi, H;Chitnis, R;Touati, A;Geramifard, A;Zhang, A;Niekum, S
通讯作者：
Niekum, S

Understanding Acoustic Patterns of Human Teachers Demonstrating Manipulation Tasks to Robots

了解人类教师向机器人演示操作任务的声学模式

DOI：
发表时间：
2022
期刊：
Proceedings of the International Conference on Intelligent Robots and Systems
影响因子：
0
作者：
Saran, A.;Desai, K.;Chang, M.L.;Lioutikov, R.;Thomaz, A.;Niekum, S.
通讯作者：
Niekum, S.

Models of human preference for learning reward functions