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.

要使机器人真正对人类有用，关键是它们能够在人类社会环境（HSEs）中独立理解和操作。 许多研究人员对这个问题进行了数十年的研究，但迄今为止未能找到解决方案。 跨两个合作机构的这个合作项目的PI认为，一个根本的范式转变是必要的，以实现进步，这种转变可以通过上下文驱动的方法来点燃移动的机器人。 因此，本提案的目标是创建和评估新的社会背景模型，以使移动的机器人适当地与HSEs中的人进行交互。 项目成果将帮助所有“会思考的东西”，从社交机器人到智能家居，更好地了解人类社会背景，并提高在现实世界人类空间中有效运作的能力。 通过贡献新的理论模型，技术和开源实现，将加速在HSE中运行的机器的开发和采用，PI预计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影响，包括机器人技术，人机交互和无处不在的计算。 PI还将向其他研究人员提供他们的上下文模型。 PI的方法是独特的，而不是以技术为中心和完全代表性的，它完全采用了Dourish的方法，以社会科学为灵感的背景-关系，动态，抽象，并从活动中产生-即，液 该方法的关键贡献在于，它将实时传感器数据与情境背景模型联系起来，然后将环境的社会启示告知机器人。 PI将从事两项主要研究活动。 首先，他们将创建一个情景上下文处理能力，可以接受来自机器人的多模态社交场景快照，并返回一个情景上下文框架（SCF），其中包含对场景的显著对象，社交活动和情景上下文的估计。 然后，他们将贡献算法技术，使机器人能够利用SCF进行引导探索，以完善其模型，并最终实现目标驱动的任务执行，以符合社会规范。 PI的研究重点是只使用自然的，真实的，多模态的数据，将使他们能够为提高多模态处理的鲁棒性做出独特的贡献。