NRI: FND: Communicating Physical Interactions

NRI：FND：沟通身体互动

• 批准号: 1830242
• 负责人: Michael Gleicher
• 金额: $ 75万
• 依托单位: University of Wisconsin-Madison
• 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: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1830242&HistoricalAwards=false
• 关键词: NRI; FND; Communicating; Physical; Interactions

For robots to become ubiquitous collaborators, they must interact physically with objects in unstructured human environments. Robots must adeptly grasp, push, squeeze, snap, balance, stabilize and hand-off objects, and must effectively communicate about these actions with their human collaborators. People must not only be able to specify to a robot what to do and how to do it, they must also be able to interpret what a robot intends to do (and how). Effective communication about physical interactions will be essential if robots are going to perform such tasks as delivering care to older adults, responsively helping technicians in performing repairs, or being trained by non-experts to perform repetitive assembly tasks. This project will enable a new generation of robot applications, and advance the vision of ubiquitous, collaborative robots by developing better methods for robots to more effectively communicate with people. The project will address key challenges in communication about physical interactions: conveying invisible and unfamiliar quantities (e.g., forces and compliances), communicating plans and contingencies, and communicating about what did not (or should not) happen. The project will involve three key challenges: specification, interpretation, and monitoring. To address these challenges, the project will 1) perform formative studies to gain insight into how people communicate about physical interactions and interpret displays; 2) develop methods for specifying physical actions based on the idea of augmented demonstrations, methods for interpreting physical actions based on the idea of interpretable representations, and methods for monitoring physical actions based on multimodal communication; and 3) deploy these ideas in prototype systems for contextualized scenarios for evaluation.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.

机器人要成为无处不在的协作者，它们必须与非结构化人类环境中的物体进行物理交互。机器人必须熟练地抓取、推动、挤压、折断、平衡、稳定和传递物体，并且必须与人类协作者有效地沟通这些动作。人们不仅必须能够向机器人指定要做什么以及如何做，还必须能够解释机器人打算做什么（以及如何做）。 如果机器人要执行诸如为老年人提供护理、快速响应地帮助技术人员进行维修或接受非专家培训以执行重复性组装任务等任务，则有关物理交互的有效沟通至关重要。 该项目将实现新一代机器人应用，并通过开发更好的机器人更有效地与人类沟通的方法来推进无处不在的协作机器人的愿景。该项目将解决物理交互沟通中的关键挑战：传达不可见和不熟悉的量（例如力和合规性）、沟通计划和突发事件以及沟通没有（或不应该）发生的事情。 该项目将涉及三个关键挑战：规范、解释和监控。 为了应对这些挑战，该项目将 1) 进行形成性研究，以深入了解人们如何交流身体互动和解释展示； 2）开发基于增强演示思想的指定物理动作的方法、基于可解释表示思想的解释物理动作的方法以及基于多模态通信的监测物理动作的方法； 3）将这些想法部署在原型系统中，以进行情境评估。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。

项目成果

Geometric Calibration of Single-Pixel Distance Sensors

单像素距离传感器的几何校准

• DOI: 10.1109/lra.2022.3176453
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Sifferman, Carter;Mehrotra, Dev;Gupta, Mohit;Gleicher, Michael
• 通讯作者: Gleicher, Michael

Characterizing the Effects of Haptic Rendering Parameter Variations on Perceived Kinesthetic Rendering Accuracy

表征触觉渲染参数变化对感知动觉渲染精度的影响

• DOI: 10.1109/whc49131.2021.9517158
• 发表时间: 2021
• 期刊: IEEE
• 作者: Zhang, Bolun;Hagenow, Michael;Mutlu, Bilge;Gleicher, Michael;Zinn, Michael
• 通讯作者: Zinn, Michael

Proxima: An Approach for Time or Accuracy Budgeted Collision Proximity Queries

Proxima：一种时间或精度预算的碰撞邻近查询方法

• DOI: 10.15607/rss.2022.xviii.043
• 发表时间: 2022
• 期刊: Proceedings of Robotics: Science and Systems (RSS
• 作者: Rakita, Daniel;Mutlu, Bilge;Gleicher, Michael
• 通讯作者: Gleicher, Michael

Designing Robotic Camera Systems to Enable Synchronous Remote Collaboration

设计机器人摄像头系统以实现同步远程协作

• 发表时间: 2023
• 期刊: HRI '23: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Praveena, Pragathi;Gleicher, Michael;Mutlu, Bilge
• 通讯作者: Mutlu, Bilge

MotionBenchMaker: A Tool to Generate and Benchmark Motion Planning Datasets

MotionBenchMaker：生成运动规划数据集并对其进行基准测试的工具

• DOI: 10.1109/lra.2021.3133603
• 发表时间: 2022
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: Chamzas, Constantinos;Quintero-Pena, Carlos;Kingston, Zachary;Orthey, Andreas;Rakita, Daniel;Gleicher, Michael;Toussaint, Marc;Kavraki, Lydia E.
• 通讯作者: Kavraki, Lydia E.