Collaborative Research: Learning to Control Dynamically Complex Objects

协作研究：学习控制动态复杂对象

• 批准号: 1826097
• 负责人: Neville Hogan
• 金额: $ 34.91万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1826097&HistoricalAwards=false
• 关键词: Collaborative; Research; Learning; Control; Dynamically

Human sensorimotor capabilities vastly out-perform those of modern robots. Prior research suggests that humans achieve skillful movement by exploiting combinations of "dynamic primitives", which are robust building blocks of coordination that simplify control. Interaction with complex objects - such as spreading a tablecloth - requires prediction which, in turn, requires mental representations of the objects and environment. This project explores the extent to which such mental models may also take the form of dynamic primitives. The project team will perform fundamental research exploring: how humans learn to manipulate a complex flexible object through the composition of dynamic primitives; the impact of explicit instruction on the acquisition of the mental models; and whether a primitive-based control structure to be implemented in a robot can achieve skillful manipulation of complex objects and fluid interactions between humans and robot. This project serves the national interest because the resulting understanding of human sensorimotor control and robot control methods may result in improved efficacy of robot-assisted physical rehabilitation after neuromotor injuries such as stroke, where safe physical cooperation with humans is fundamental and mutual learning is of paramount importance. The project will involve an educational component that provides engineering and research methods training to graduate and undergraduate students, as well as STEM outreach to individuals of all ages at the Museum of Science in Boston. Additional efforts will be made to attract and retain women into careers in science and engineering.This research investigates a biomimetic approach to robot control that promise improved human-robot physical interaction during co-manipulation of flexible objects with complex continuum dynamics. Methods include skill acquisition experiments involving expert and novice human subjects to test how mental representations are formed and the extent to which they may be shaped by explicit instruction during training; a theoretical study of how motor learning may be facilitated using mental models based on dynamic primitives vs. lumped mechanical properties; and a study of human-robot co-manipulation of flexible objects wherein dynamic primitives provide the basis not only for robotic control, but also for communication and mutual learning between the human and robot partners. This research promises new insights into the manipulation of complex objects and tools, an area where humans still outperform machines.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.

人类的感觉运动能力远远超过现代机器人。先前的研究表明，人类通过利用“动态原语”的组合来实现熟练的运动，这些原语是简化控制的协调的强大构建块。与复杂物体的互动——比如铺桌布——需要预测，这反过来又需要对物体和环境的心理表征。这个项目探索了这种心理模型在多大程度上也可以采用动态原语的形式。项目团队将进行基础研究探索：人类如何通过动态原语的组合来学习操纵复杂的柔性对象；外显教学对心理模型习得的影响在机器人中实现的基于原始的控制结构是否能够实现对复杂物体的熟练操作以及人与机器人之间的流体交互。该项目符合国家利益，因为由此产生的对人类感觉运动控制和机器人控制方法的理解可能会提高中风等神经运动损伤后机器人辅助肢体康复的疗效，在这种情况下，与人类安全的身体合作是最基本的，相互学习是至关重要的。该项目将包括一个教育部分，为研究生和本科生提供工程和研究方法培训，并在波士顿科学博物馆向所有年龄段的个人提供STEM推广。将作出更多努力，吸引和留住妇女从事科学和工程事业。本研究探讨了一种仿生机器人控制方法，该方法有望在复杂连续动力学的柔性物体协同操作过程中改善人机物理交互。方法包括涉及专家和新手的人类受试者的技能习得实验，以测试心理表征是如何形成的，以及在训练期间明确指示对其形成的程度；如何使用基于动态原语与集总机械特性的心理模型促进运动学习的理论研究；在柔性物体的人-机器人协同操作研究中，动态原语不仅为机器人控制提供了基础，而且为人与机器人之间的交流和相互学习提供了基础。这项研究有望为复杂物体和工具的操作提供新的见解，这是人类仍然胜过机器的领域。该奖项反映了美国国家科学基金会的法定使命，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

Control of goal-directed movements within (or beyond) reach?

控制目标导向的运动在（或超出）范围内？

• DOI: 10.1016/j.plrev.2019.03.016
• 发表时间: 2019
• 期刊: Physics of Life Reviews
• 影响因子: 11.7
• 作者: Sternad, Dagmar;Hogan, Neville
• 通讯作者: Hogan, Neville

Online Impedance Adaptation Facilitates Manipulating a Whip

在线阻抗适应有助于操纵鞭子

• DOI: 10.1109/iros51168.2021.9636663
• 发表时间: 2021
• 期刊: IEEE International Conference on Intelligent Robots and Systems
• 作者: Xiong, Xiaofeng;Nah, Moses C.;Krotov, Aleksei;Sternad, Dagmar
• 通讯作者: Sternad, Dagmar

Enhancing Robot-Environment Physical Interaction via Optimal Impedance Profiles

通过最佳阻抗分布增强机器人与环境的物理交互

• 发表时间: 2020
• 期刊: IEEE International Conference on Biomedical Robotics and Biomechatronics
• 作者: Averta, Giuseppe;Hogan, Neville
• 通讯作者: Hogan, Neville

Transient Behavior and Predictability in Manipulating Complex Objects

操纵复杂对象时的瞬态行为和可预测性

• DOI: 10.1109/icra40945.2020.9196977
• 发表时间: 2020
• 期刊: IEEE International Conference on Robotics and Automation (ICRA 2020
• 作者: Nayeem, Rashida;Bazzi, Salah;Hogan, Neville;Sternad, Dagmar
• 通讯作者: Sternad, Dagmar

Separating neural influences from peripheral mechanics: the speed-curvature relation in mechanically constrained actions

• DOI: 10.1152/jn.00536.2019
• 发表时间: 2020-05-01
• 期刊: JOURNAL OF NEUROPHYSIOLOGY
• 影响因子: 2.5
• 作者: Hermus, James;Doeringer, Joseph;Hogan, Neville
• 通讯作者: Hogan, Neville