CHS: Small: Collaborative Research: Modeling Social Context to Improve Human-Robot Interaction

CHS：小型：协作研究：建模社会环境以改善人机交互

• 批准号: 1527759
• 负责人: Laurel Riek
• 金额: $ 25万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527759&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Modeling

For robots to be truly useful to people, it is critical that they be able to understand and operate independently in human social environments (HSEs). Decades of research into this problem by many investigators have to date failed to yield a solution. The PIs on this collaborative project that spans two partner institutions argue that a fundamental paradigm shift is necessary to enable progress, and that this shift can be ignited through a contextually driven approach to mobile robotics. Thus, the goal of this proposal is to create and evaluate new models of social context in order to enable mobile robots to interact appropriately with people in HSEs. Project outcomes will help give all "things that think", from social robots to smart homes, a better understanding of human social context and a greater capability to operate effectively in real-world human spaces. By contributing new theoretical models, techniques, and open source implementations that will accelerate the development and adoption of machines that operate in HSEs, the PIs anticipate this work will have a transformative impact on many fields within computer and information science, including robotics, human-machine interaction, and ubiquitous computing. The PIs also will make their context models available to other researchers. The PIs' approach to context is unique in that, rather than being techno-centric and entirely representational, it fully adopts Dourish's approach to an interactional model of context inspired by the social sciences - relational, dynamic, occasioned, and arising from activity - i.e., fluid. The key contribution of the approach is that it ties real-time sensor data to models of situational context, which then inform a robot of the social affordances of the environment. The PIs will engage in two primary research activities. First, they will create a situational context processing capability that can accept a multimodal, social scene snapshot from a robot and return back a situational context frame (SCF) that contains an estimate of the scene's salient objects, social activities, and situational context. Then, they will contribute algorithmic techniques that enable a robot to leverage the SCF to perform guided exploration to refine its model, and ultimately, goal-driven task execution adapted to conform to social norms. The PIs' research emphasis on using solely naturalistic, real-world, multimodal data, will enable them to make unique contributions for increasing robustness in multimodal processing.

对于机器人对人的真正有用，至关重要的是，他们能够在人类的社会环境（HSE）中独立理解和运作。 迄今为止，许多研究人员对此问题进行了数十年的研究，未能产生解决方案。 跨越两个合作伙伴机构的合作项目的PI认为，必须进行基本范式转变，以实现进步，并且可以通过上下文驱动的移动机器人技术来点燃这种转变。 因此，该提案的目的是创建和评估社会环境的新模型，以使移动机器人能够与HSES中的人进行适当的互动。 项目成果将有助于提供从社会机器人到智能家园，对人类社会环境的更好理解以及在现实世界中有效运作的能力。 通过贡献新的理论模型，技术和开源实现，这些模型将加速在HSES中运行的机器的开发和采用，PIS预测这项工作将对计算机和信息科学中的许多领域产生变革性的影响，包括机器人技术，人机相互作用和普遍计算。 PI还将使其上下文模型可用于其他研究人员。 PIS的上下文方法是独一无二的，因为它不是以技术为中心的，并且完全具有代表性，而是完全采用了Dourish的方法，可以采用受社会科学启发的背景模型的互动模型 - 关系，动态，偶然，伴随着活动 - 即流体。 该方法的关键贡献是将实时传感器数据与情境环境模型联系起来，然后将机器人告知环境的社会负担。 PI将进行两项主要研究活动。 首先，他们将创建一个情境上下文处理能力，该功能可以接受来自机器人的多模式的社交场景快照，并返回一个情境上下文框架（SCF），其中包含对场景的显着对象，社交活动和情境环境的估计。 然后，他们将贡献算法技术，使机器人能够利用SCF进行指导探索，以完善其模型，并最终以目标驱动的任务执行为符合社会规范。 PIS的研究重点是仅使用自然主义，现实世界中的多模式数据，这将使他们能够为增加多模式处理的鲁棒性做出独特的贡献。