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: 1720713
• 负责人: Laurel Riek
• 金额: $ 16.13万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-16 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1720713&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.

机器人要真正对人类有用，至关重要的是它们能够在人类社会环境（HSE）中理解并独立操作。 许多研究人员对这个问题进行了数十年的研究，迄今为止仍未能找到解决方案。 这个跨越两个合作机构的合作项目的 PI 认为，要取得进展，必须进行根本性的范式转变，并且可以通过情境驱动的移动机器人方法来引发这种转变。 因此，该提案的目标是创建和评估新的社会环境模型，以使移动机器人能够与 HSE 中的人员进行适当的交互。 项目成果将有助于让从社交机器人到智能家居的所有“会思考的事物”更好地理解人类社会背景，并增强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，加速 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的许多领域产生变革性影响，包括机器人技术、人类学机器交互和普适计算。 PI 还将向其他研究人员提供他们的上下文模型。 PI 处理情境的方法是独特的，因为它不是以技术为中心且完全具有代表性，而是完全采用了 Dourish 的方法来构建受社会科学启发的情境交互模型 - 关系性、动态性、偶然性和源于活动 - 即， 体液。 该方法的主要贡献在于，它将实时传感器数据与情境上下文模型联系起来，然后将环境的社会可供性告知机器人。 PI 将参与两项主要研究活动。 首先，他们将创建情景上下文处理功能，该功能可以接受来自机器人的多模式社交场景快照，并返回情景上下文框架 (SCF)，其中包含场景显着对象、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 的研究重点是仅使用自然的、真实世界的多模态数据，这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。