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

机器人要真正对人类有用，关键是它们能够理解并在人类社会环境(HSE)中独立操作。到目前为止，许多研究人员对这个问题进行了数十年的研究，但未能得出解决方案。这个跨越两个合作机构的协作项目的PI认为，为了取得进展，有必要进行根本性的范式转变，这种转变可以通过对移动机器人的背景驱动方法来点燃。因此，这项提议的目标是创建和评估新的社会背景模型，以便使移动机器人能够适当地与HSE中的人进行交互。项目成果将有助于让从社交机器人到智能家居的所有“会思考的东西”更好地理解人类的社会背景，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，将加速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中的许多领域产生革命性影响，包括机器人学、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背景模型。私人投资机构对语境的方法是独一无二的，它不是以技术为中心，完全是代表性的，它完全采用了杜里什的方法，建立了受社会科学启发的语境互动模型--关系的、动态的、偶然的、产生于活动的--即流动的。该方法的关键贡献在于，它将实时传感器数据与情景环境模型联系起来，然后将环境的社会负担告知机器人。私人投资主任将从事两项主要的研究活动。首先，他们将创建一种情境处理能力，该能力可以接受来自机器人的多模式社交场景快照，并返回包含场景显著对象、社交活动和情境情境的估计的情境情境框架(SCF)。然后，他们将贡献算法技术，使机器人能够利用SCF执行指导探索，以完善其模型，并最终实现符合社会规范的目标驱动任务执行。PI的研究重点是只使用自然的、真实的、多模式的数据，这将使他们能够为提高多模式处理的健壮性做出独特的贡献。

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.