CAREER: Robot Learning from Motor-Impaired Instructors and Task Partners

职业:机器人向运动障碍教练和任务伙伴学习

基本信息

  • 批准号:
    1552706
  • 负责人:
  • 金额:
    $ 52.5万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-02-01 至 2023-01-31
  • 项目状态:
    已结题

项目摘要

Assistive machines - like power wheelchairs, robotic arms, exoskeletons, prostheses - are vital for enabling independence for people with severe motor impairments. However, there exists a paradox, where often the more severe a person's impairment, the less able they are to operate these very machines which might improve their quality of life. Here robotics technologies have the potential to transform the field of human health and rehabilitation: by turning the machine into a robot, that can operate itself autonomously and share the control burden. It will be crucial that these robots adapt to the human user's unique preferences and abilities, and how both change over time is crucial for achieving widespread adoption and acceptance, and especially if attached to a human's body to provide physical assistance. There has been limited study of robot learning from non-experts, and the domain of motor-impaired teachers is even more challenging: their control signals are noisy (due to artifacts in the motor signal) and sparse (if providing motor commands is more effort), and filtered through an interface. Rather than treat these constraints as limitations, the proposed work hypothesizes that such constraints become advantageous for machine learning algorithms that exploit unique characteristics (like problem-space sparsity) of control and feedback signals from motor-impaired humans. The work develops multiple novel machine learning algorithmic techniques, (1) that reason explicitly about the control interface and how it interacts with the full robot control space; (2) that derive information about the human's control patterns and task requirements, from variability in the human's teleoperation commands; and (3) which include the design of adaptation cues informed by reward- and example-based feedback from motor-impaired teachers. The proposed work also performs subject studies with motor-impaired end-users operating multiple robotic platforms, both to explore this problem space and assess the functionality and user acceptance of the contributed algorithmic techniques.
辅助机器--如电动轮椅、机械臂、外骨骼、假肢--对于使有严重运动障碍的人能够独立至关重要。然而,存在一个悖论,通常一个人的损伤越严重,他们操作这些机器的能力就越差,而这些机器可能会改善他们的生活质量。在这里,机器人技术有可能改变人类健康和康复领域:通过将机器变成机器人,可以自主操作并分担控制负担。这些机器人必须适应人类用户的独特偏好和能力,而这两者如何随着时间的推移而变化,对于实现广泛的采用和接受至关重要,特别是如果连接到人体上提供物理帮助。对于机器人从非专家那里学习的研究有限,而运动障碍教师的领域更具挑战性:他们的控制信号是嘈杂的(由于运动信号中的伪影)和稀疏的(如果提供运动命令是更多的努力),并通过界面过滤。而不是把这些约束作为限制,提出的工作假设,这样的约束变得有利于机器学习算法,利用独特的特征(如问题空间稀疏)的控制和反馈信号从运动受损的人。这项工作开发了多种新的机器学习算法技术,(1)明确地推理控制界面以及它如何与整个机器人控制空间交互;(2)从人类遥控操作命令的变化中获得有关人类控制模式和任务要求的信息;(3)包括由运动障碍教师提供的基于奖励和样例的反馈信息设计适应线索。拟议的工作还进行了主题研究与电机受损的终端用户操作多个机器人平台,既探索这个问题的空间,并评估功能和用户接受的贡献算法技术。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Brenna Argall其他文献

Wheelchair Interface Usage Assessment Tasks and Performance Measures for Assistive Robots
  • DOI:
    10.1016/j.apmr.2019.08.443
  • 发表时间:
    2019-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Mahdieh Nejati Javaremi;Michael Young;Brenna Argall
  • 通讯作者:
    Brenna Argall

Brenna Argall的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Brenna Argall', 18)}}的其他基金

NSF Convergence Accelerator: Track H: Mobility Independence through Accelerated Wheelchair Intelligence
NSF 融合加速器:轨道 H:通过加速轮椅智能实现移动独立
  • 批准号:
    2345174
  • 财政年份:
    2023
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Cooperative Agreement
Interface-Aware Intelligence for Robot Teleoperation and Autonomy
用于机器人远程操作和自主的接口感知智能
  • 批准号:
    2208011
  • 财政年份:
    2022
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant
NSF Convergence Accelerator: Track H: Mobility Independence through Accelerated Wheelchair Intelligence
NSF 融合加速器:轨道 H:通过加速轮椅智能实现移动独立
  • 批准号:
    2236354
  • 财政年份:
    2022
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant
CPS: Synergy: Collaborative Research: Learning control sharing strategies for assistive cyber-physical systems
CPS:协同:协作研究:辅助网络物理系统的学习控制共享策略
  • 批准号:
    1544741
  • 财政年份:
    2015
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant

相似海外基金

CAREER: Closing the Loop between Learning and Communication for Assistive Robot Arms
职业:关闭辅助机器人手臂的学习和交流之间的循环
  • 批准号:
    2337884
  • 财政年份:
    2024
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant
CAREER: Democratizing Robot Learning for Assistive Robotics in MCI
职业:MCI 辅助机器人的机器人学习民主化
  • 批准号:
    2340177
  • 财政年份:
    2024
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
CAREER: Concurrent Robot Learning from Simulation and Real for Closing the Sim-to-real Gap
职业:机器人从模拟和真实中并行学习,以缩小模拟与真实的差距
  • 批准号:
    2339076
  • 财政年份:
    2024
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
CAREER: Robot Learning of Complex Tasks via Skill Reusability and Refinement
职业:机器人通过技能的可重用性和改进来学习复杂的任务
  • 批准号:
    2237463
  • 财政年份:
    2023
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
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
  • 批准号:
    2323384
  • 财政年份:
    2023
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
CAREER: Enhancing Robot Physical Intelligence via Crowdsourced Surrogate Learning
职业:通过众包代理学习增强机器人物理智能
  • 批准号:
    1944069
  • 财政年份:
    2020
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant
CAREER: Reinforcement-Learning Assist-As-Needed Control For Robot-Assisted Gait Training
职业:机器人辅助步态训练的强化学习辅助按需控制
  • 批准号:
    1944203
  • 财政年份:
    2020
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Standard Grant
CAREER: Learning Symbolic Representations for Robot Manipulation
职业:学习机器人操作的符号表示
  • 批准号:
    1844960
  • 财政年份:
    2019
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
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
  • 批准号:
    1749204
  • 财政年份:
    2018
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
CAREER: Preventive Robotics: Learning and Adaptation for Predictive Human Robot Symbiosis
职业:预防性机器人技术:预测性人类机器人共生的学习和适应
  • 批准号:
    1749783
  • 财政年份:
    2018
  • 资助金额:
    $ 52.5万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了