S&AS: INT: Socially-Aware Autonomy for Long-Term Deployment of Always-On Heterogeneous Robot Teams
S&AS: INT: Socially-Aware Autonomy for Long-Term Deployment of Always-On Heterogeneous Robot Teams
批准号:
1724157
负责人:
Scott Niekum
金额:
$110.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-09-01 至 2022-08-31
中文摘要
点击翻译按钮获取中文摘要
英文摘要
This project studies how best to enable a team of mobile robots to fully integrate into the social fabric of a community of people. As such, the most important impact of this project is the ongoing development and dissemination of core algorithms that, for the first time, enable robots to be fully integrated in a socially-aware way into a community of people over a long period of time. In addition to many traditional impacts on education, including a curriculum for a project-based course, serving as a platform for graduate and undergraduate research, this research is being used as the basis for extensive public outreach via demonstrations, camps, and short courses, with a particular focus on underrepresented groups such as women in Computer Science and Engineering and Hispanic students.This project focusses on three main technical challenges that are crucial to enabling robots to integrate into the social fabric of a community. First, the project is working towards enabling robots to be cognizant about dynamic social environments. Second, the project is working towards enabling robots to reason about heterogeneous human-robot capabilities while carrying out specific tasks. Third, the project is working towards enabling safe, reflective lifelong learning in dynamic environments. In addition to leading to focussed algorithms designed to address these specific challenges, the project contributions are also being instantiated within a single, coherent multi-robot system capable of long-term autonomy. A key facilitator of the project's validation plan is a flexible, adaptable, and reusable software and hardware infrastructure that is being developed and expanded at UT Austin under an NSF Computing Research Infrastructure program grant. The main experimental objective of the project is to demonstrate that these robots can not only operate autonomously for long periods of time, but also be fully integrated into and accepted by the community of people who use the building in which they are situated.
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DOI:
--
发表时间:
2020-07
期刊:
ArXiv
影响因子:
--
作者:
[Prasoon Goyal;S. Niekum;R. Mooney]
通讯作者:
Prasoon Goyal;S. Niekum;R. Mooney
SCAPE: Learning Stiffness Control from Augmented Position Control Experiences
SCAPE:从增强的位置控制经验中学习刚度控制
DOI:
--
发表时间:
2021
期刊:
Conference on Robot Learning
影响因子:
--
作者:
[Kim, M, Niekum, S, Deshpande, A]
通讯作者:
Deshpande, A
DOI:
--
发表时间:
2019-07
期刊:
影响因子:
--
作者:
[Daniel S. Brown;Wonjoon Goo;S. Niekum]
通讯作者:
Daniel S. Brown;Wonjoon Goo;S. Niekum
DOI:
--
发表时间:
2019-07
期刊:
J. Mach. Learn. Res.
影响因子:
--
作者:
[Oliver Kroemer;S. Niekum;G. Konidaris]
通讯作者:
Oliver Kroemer;S. Niekum;G. Konidaris
DOI:
10.24963/ijcai.2020/689
发表时间:
2020-07
期刊:
IJCAI : proceedings of the conference
影响因子:
--
作者:
[Ruohan Zhang;Akanksha Saran;Bo Liu-;Yifeng Zhu;Sihang Guo;S. Niekum;D. Ballard;M. Hayhoe]
通讯作者:
Ruohan Zhang;Akanksha Saran;Bo Liu-;Yifeng Zhu;Sihang Guo;S. Niekum;D. Ballard;M. Hayhoe
共 33 条
CAREER: Safe and Efficient Robot Learning from Demonstration in the Real World
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批准号:2323384
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项目类别:Continuing Grant
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资助金额:$52.46万
-
财政年份:2023
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负责人:Scott Niekum
-
依托单位:
CAREER: Safe and Efficient Robot Learning from Demonstration in the Real World
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批准号:1749204
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项目类别:Continuing Grant
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资助金额:$52.46万
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财政年份:2018
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负责人:Scott Niekum
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依托单位:
NRI: Collaborative Research: Scalable Robot Autonomy through Remote Operator Assistance and Lifelong Learning
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批准号:1638107
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项目类别:Standard Grant
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资助金额:$48.63万
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财政年份:2016
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负责人:Scott Niekum
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依托单位:
RI: Small: High Confidence, Efficient Learning Under Rich Task Specifications
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批准号:1617639
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项目类别:Standard Grant
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资助金额:$47.0万
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财政年份:2016
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负责人:Scott Niekum
-
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国内基金
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