NRI: FND: Spatial Patterns of Behavior in Human-Robot Interaction Under Environmental Spatial Constraints

NRI：FND：环境空间约束下人机交互行为的空间模式

• 批准号: 1924802
• 负责人: Marynel Vazquez
• 金额: $ 49.91万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1924802&HistoricalAwards=false
• 关键词: NRI; FND; Spatial; Patterns; Behavior

This project promotes the progress of science and robotics by advancing autonomous reasoning about spatial patterns of group behavior during human-robot conversations. Spatial patterns emerge during conversations as a result of every participant's need to communicate while simultaneously perceiving everyone else's response. For example, circular conversational groups often emerge in open spaces. In physically constrained spaces, though, people's position might be influenced by nearby elements, such as walls and other people, leading to variations in acceptable group structure. To enable robots to cope with this variability, the project provides the empirical knowledge and methods needed to incorporate spatial constraints into the way robots reason about human (and robot) spatial formations. The project outcomes have implications across socially-relevant application domains in which user acceptance of co-robots can have a positive impact, including mobile service applications, education, and healthcare. Research activities will offer training opportunities to broaden participation in computing, serve to mentor and train future roboticists, and engage the public in the science of robotics.Building on foundational work in Human-Robot Interaction (HRI), this project addresses three main questions to advance perception and decision-making for co-robots in group settings: (1) how do spatial constraints influence conversational group formations in HRI; (2) how can robots detect these formations under spatial constraints; and (3) how can they autonomously generate appropriate spatial behavior to sustain conversations in constrained environments. To this end, this research will first focus on a formative study to better understand the effect of spatial constraints on group formations in HRI. This effort will result in a new public dataset of group-robot interactions that can be used to benchmark group detection approaches. The data will contribute to lowering barriers of entry to studying group human-robot interaction. Then, new methods for detecting spatial group formations in HRI will be developed by combining model-based and data-driven learning methods. Special consideration will be given to identifying groups in constrained environments. Finally, the project will investigate mechanisms to enable robots to take part in group formations under varying environmental spatial constraints. This last effort will help co-robots communicate with users and sustain group conversations by physically adapting to the environment. Together the outcomes of the project will help robots cope with the inherent complexity of multi-party interactions.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.

该项目通过推进人类与机器人对话过程中群体行为空间模式的自主推理，促进了科学和机器人技术的进步。空间模式出现在对话过程中，因为每个参与者都需要在交流的同时感知其他人的反应。例如，圆形对话团体经常出现在开放空间中。然而，在物理限制的空间中，人们的位置可能会受到附近元素的影响，例如墙壁和其他人，导致可接受的群体结构的变化。为了使机器人能够科普这种变化，该项目提供了将空间约束纳入机器人对人类（和机器人）空间形态的推理方式所需的经验知识和方法。该项目的成果对社交相关的应用领域具有影响，其中用户对协作机器人的接受可以产生积极的影响，包括移动的服务应用，教育和医疗保健。研究活动将提供培训机会，以扩大参与计算，服务于导师和培训未来的机器人学家，并从事公众在机器人学的science.Building的基础工作，在人-机器人交互（HRI），该项目解决了三个主要问题，以提高感知和决策的合作机器人在群体设置：（1）空间约束如何影响会话组形成在HRI;（2）机器人如何在空间约束下检测这些编队;（3）它们如何自主地产生适当的空间行为以在受限环境中维持会话。为此，本研究将首先集中在一个形成性研究，以更好地了解在HRI组形成的空间限制的效果。这一努力将产生一个新的组机器人交互的公共数据集，可用于基准组检测方法。这些数据将有助于降低研究群体人机交互的门槛。然后，新的方法来检测HRI空间组的形成将开发结合基于模型和数据驱动的学习方法。将特别考虑在受限制的环境中确定群体。最后，该项目将研究机制，使机器人参加在不同的环境空间限制下的群体形成。这最后一项工作将帮助协作机器人与用户沟通，并通过身体适应环境来维持群体对话。该项目的成果将帮助机器人科普多方互动的固有复杂性。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

Conversational Group Detection with Graph Neural Networks

• DOI: 10.1145/3462244.3479963
• 发表时间: 2021-10
• 期刊: Proceedings of the 2021 International Conference on Multimodal Interaction
• 作者: Sydney Thompson;Abhijit Gupta;Anjali W. Gupta;Austin Chen;Marynel Vázquez

Improving the Robustness of Social Robot Navigation Systems

提高社交机器人导航系统的鲁棒性

• DOI: 10.1145/3434074.3446360
• 发表时间: 2021
• 期刊: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Tsoi, Nathan;Vázquez, Marynel
• 通讯作者: Vázquez, Marynel

Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers

• 发表时间: 2020-09
• 作者: Nathan Tsoi;Kate Candon;Deyuan Li;Yofti Milkessa;M. V'azquez

Improving Social Awareness Through DANTE: Deep Affinity Network for Clustering Conversational Interactants

通过 DANTE 提高社交意识：用于聚类对话交互者的深度亲和力网络

• DOI: 10.1145/3392824
• 发表时间: 2020
• 期刊: Proceedings of the ACM on Human-Computer Interaction
• 作者: Swofford, Mason;Peruzzi, John;Tsoi, Nathan;Thompson, Sydney;Martín-Martín, Roberto;Savarese, Silvio;Vázquez, Marynel
• 通讯作者: Vázquez, Marynel

Self-Annotation Methods for Aligning Implicit and Explicit Human Feedback in Human-Robot Interaction

• DOI: 10.1145/3568162.3576986
• 发表时间: 2023-03
• 期刊: Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Qiping Zhang;Austin Narcomey;Kate Candon;Marynel Vázquez