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

基本信息

  • 批准号:
    1749783
  • 负责人:
  • 金额:
    $ 49.96万
  • 依托单位:
  • 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:
    2018-08-01 至 2023-07-31
  • 项目状态:
    已结题

项目摘要

Deploying assistive technologies that intelligently minimize the risk of musculoskeletal injury during physical tasks could improve user safety and significantly reduce the healthcare costs associated with the treatment of long-term disabilities such as chronic pain. With that goal in mind, this CAREER project will contribute key innovations that allow robots to reason about the biomechanical safety of actions performed jointly with a human partner. The research will focus on the concept of Preventive Robotics, a novel approach to human-machine collaboration that incorporates the biomechanical well-being of the human user into robot control and decision-making. In contrast to Rehabilitation Robotics, which focuses on therapeutic procedures after an injury occurs, Preventive Robotics seeks to proactively reduce the risk of injury. A critical knowledge gap in this regard is the absence of a theoretical foundation that supports human-machine symbiosis - healthy, physical, and bi-directional interactions between human and machine which can be comfortably sustained over very long periods of time. The main objective of Preventive Robotics is to generate assistive robot actions that (a) seamlessly blend with actions of the human partner to achieve the intended function, while (b) minimizing biomechanical stress on the human body. Coalescing these two goals will unlock new potential for robotics to drastically improve public and occupational health. The project will also involve transition of innovations to a commercial partner developing intelligent lower-leg prostheses. The research integrates with an education program targeting K-12 students, undergraduate and graduate students, and students from underrepresented groups. To these ends, the project will develop a unified Bayesian framework for modeling symbiotic dynamics among multiple agents using a compact probabilistic and data-driven methodology. The framework will bridge the divide between predictive modeling of humans and predictive control of symbiotic human-robot systems. A Bayesian representation will be used to derive algorithms for learning and adaptation which include the future biomechanical state of a human user. In addition, new symbiotic control algorithms will be introduced that utilize predicted biomechanical variables to steer the human-robot interaction towards biomechanically safe movement regimes. These control methods will provide new insights about strongly-coupled systems with reciprocal dependencies, in which only one system can be actively controlled (e.g., an assistive device or prosthesis). The new approach will be implemented on a powered-ankle prosthesis in order to anticipate joint loads and proactively avoid high stresses. The resulting prosthesis will have the potential to significantly lower the risk of musculoskeletal diseases such as osteoarthritis.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.
部署智能地最大限度地降低体力任务期间肌肉骨骼损伤风险的辅助技术可以提高用户安全性,并显着降低与治疗慢性疼痛等长期残疾相关的医疗成本。 考虑到这一目标,这个CAREER项目将贡献关键的创新,使机器人能够推理与人类合作伙伴共同执行的动作的生物力学安全性。 该研究将重点关注预防机器人的概念,这是一种人机协作的新方法,将人类用户的生物力学健康纳入机器人控制和决策中。 与专注于受伤后的治疗程序的康复机器人技术相反,预防机器人技术旨在主动降低受伤的风险。 在这方面,一个关键的知识差距是缺乏支持人机共生的理论基础-人类和机器之间健康,物理和双向的互动,可以在很长一段时间内舒适地持续下去。 预防机器人的主要目标是生成辅助机器人动作,(a)与人类伙伴的动作无缝融合,以实现预期的功能,同时(B)最大限度地减少对人体的生物力学应力。 将这两个目标结合起来,将为机器人技术释放新的潜力,以大幅改善公共和职业健康。 该项目还将涉及将创新转移给开发智能小腿假肢的商业合作伙伴。 该研究与针对K-12学生,本科生和研究生以及代表性不足群体的学生的教育计划相结合。 为此,该项目将开发一个统一的贝叶斯框架,用于使用紧凑的概率和数据驱动的方法来建模多个代理之间的共生动力学。该框架将弥合人类预测建模和共生人机系统预测控制之间的鸿沟。贝叶斯表示将用于推导学习和适应算法,其中包括人类用户的未来生物力学状态。此外,将引入新的共生控制算法,利用预测的生物力学变量来引导人机交互向生物力学安全的运动机制。这些控制方法将提供关于具有相互依赖性的强耦合系统的新见解,其中只有一个系统可以被主动控制(例如,辅助装置或假体)。新方法将在动力踝关节假体上实施,以预测关节负荷并主动避免高应力。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(10)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
alpha-MDF: An Attention-based Multimodal Differentiable Filter for Robot State Estimation
alpha-MDF:用于机器人状态估计的基于注意力的多模态可微分滤波器
Learning and Blending Robot Hugging Behaviors in Time and Space
学习和融合机器人在时间和空间上的拥抱行为
  • DOI:
    10.1109/icra48891.2023.10160587
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Drolet, Michael;Campbell, Joseph;Amor, Heni Ben
  • 通讯作者:
    Amor, Heni Ben
Learning Interactive Behaviors for Musculoskeletal Robots Using Bayesian Interaction Primitives
使用贝叶斯交互原语学习肌肉骨骼机器人的交互行为
Learning modular language-conditioned robot policies through attention
  • DOI:
    10.1007/s10514-023-10129-1
  • 发表时间:
    2023-08
  • 期刊:
  • 影响因子:
    3.5
  • 作者:
    Yifan Zhou;Shubham D. Sonawani;Mariano Phielipp;Heni Ben Amor-Heni-Ben Amor-2236830725;Simon Stepputtis
  • 通讯作者:
    Yifan Zhou;Shubham D. Sonawani;Mariano Phielipp;Heni Ben Amor-Heni-Ben Amor-2236830725;Simon Stepputtis
Learning Ergonomic Control in Human–Robot Symbiotic Walking
学习人类与机器人共生行走的人体工学控制
  • DOI:
    10.1109/tro.2022.3192779
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    7.8
  • 作者:
    Clark, Geoffrey;Ben Amor, Heni
  • 通讯作者:
    Ben Amor, Heni
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Heni Ben Amor其他文献

Workshop Report: Novel and Emerging Test Methods and Metrics for Effective HRI, ACM/IEEE Conference on Human-Robot Interaction, 2021
研讨会报告:有效 HRI 的新颖和新兴测试方法和指标,ACM/IEEE 人机交互会议,2021 年
  • DOI:
    10.6028/nist.ir.8417
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Shelly Bagchi;Jeremy A. Marvel;M. Zimmerman;Murat Aksu;Brian Antonishek;Xiang Li;Heni Ben Amor;T. Fong;Ross Mead;Yue Wang
  • 通讯作者:
    Yue Wang
Workshop Report: Test Methods and Metrics for Effective HRI in Collaborative Human-Robot Teams, ACM/IEEE Human-Robot Interaction Conference, 2019
研讨会报告:人机协作团队中有效 HRI 的测试方法和指标,ACM/IEEE 人机交互会议,2019 年
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Shelly Bagchi;Murat Aksu;M. Zimmerman;Jeremy A. Marvel;Brian Antonishek;Heni Ben Amor;T. Fong;Ross Mead;Yue Wang
  • 通讯作者:
    Yue Wang
Special issue on learning for human–robot collaboration
  • DOI:
    10.1007/s10514-018-9756-z
  • 发表时间:
    2018-04-26
  • 期刊:
  • 影响因子:
    4.300
  • 作者:
    Leonel Rozo;Heni Ben Amor;Sylvain Calinon;Anca Dragan;Dongheui Lee
  • 通讯作者:
    Dongheui Lee

Heni Ben Amor的其他文献

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{{ truncateString('Heni Ben Amor', 18)}}的其他基金

CPS: Medium: Collaborative Research: Learning and Verifying Conformant Data-Driven Models for Cyber-Physical Systems
CPS:媒介:协作研究:学习和验证网络物理系统的一致数据驱动模型
  • 批准号:
    1932068
  • 财政年份:
    2019
  • 资助金额:
    $ 49.96万
  • 项目类别:
    Standard Grant

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5G4PHealth:增强型 5G 支持平台,用于预测、预防、个性化和参与式医疗保健
  • 批准号:
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    2024
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Prevention of third party damages caused by peeling of building finishing materials of outer wall and exterior panels -Establishment of basic technology for preventive maintenance-
防止因外墙和外板建筑装饰材料剥落而造成的第三方损害 -建立预防性维护的基本技术-
  • 批准号:
    23H01552
  • 财政年份:
    2023
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    Grant-in-Aid for Scientific Research (B)
Development of novel macrocyclic BACE1 inhibitors for preventive or therapeutic agents for Alzheimer's disease
开发用于预防或治疗阿尔茨海默病的新型大环 BACE1 抑制剂
  • 批准号:
    23K06058
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Exploration of immunogenetic and molecular basis of hypertensive disease aiming at establishment of novel preventive and therapeutic strategies
探索高血压疾病的免疫遗传学和分子基础,旨在建立新的预防和治疗策略
  • 批准号:
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IL-1RA在口腔癌放疗增敏和预防粘膜炎中的应用。
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研究减少癌症治疗中辐射损伤的预防和治疗方法
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