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

该项目通过在人机对话过程中推进对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。在对话过程中，由于每个参与者都需要交流，同时感知其他人的反应，空间模式就会出现。例如，循环对话组经常出现在开放空间中。然而，在物理上受限的空间中，人们的位置可能会受到附近元素（如墙壁和其他人）的影响，从而导致可接受的群体结构发生变化。为了使机器人能够应对这种可变性，该项目提供了将空间约束纳入机器人对人类（和机器人）空间形成的推理方式所需的经验知识和方法。项目成果在与社会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影响，包括移动服务应用、教育和医疗保健。研究活动将提供培训机会，以扩大对计算机的参与，指导和培训未来的机器人专家，并使公众参与机器人科学。基于人机交互（HRI）的基础工作，本项目解决了三个主要问题，以促进群体环境下协作机器人的感知和决策：(1)空间约束如何影响人机交互中会话群体的形成；(2)机器人如何在空间约束下检测这些地层；(3)他们如何自主地产生适当的空间行为来维持在受限环境中的对话。为此，本研究将首先侧重于形成性研究，以更好地理解空间约束对HRI群体形成的影响。这一努力将产生一个新的群体-机器人互动的公共数据集，可用于基准群体检测方法。这些数据将有助于降低研究群体人机交互的门槛。然后，将基于模型的学习方法与数据驱动的学习方法相结合，开发HRI中空间群形成检测的新方法。将特别考虑确定受限制环境中的群体。最后，该项目将研究使机器人在不同环境空间约束下参与群体形成的机制。最后的努力将有助于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。该项目的成果将有助于机器人应对多方互动的固有复杂性。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。

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.