A Cognition-based Model for More Forgiving Human-Machine Interactions through Embodied Cooperation

基于认知的模型，通过具体合作实现更宽容的人机交互

• 批准号: 2211906
• 负责人: Jonathon Schofield
• 金额: $ 90.11万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2211906&HistoricalAwards=false
• 关键词: Cognition; based; Model; More; Forgiving

This award will provide a new framework to promote more forgiving relationships between humans and autonomous machines by blurring the lines between the machine, the operator, and their cooperative actions. As humans we are wired to effectively use autonomous systems as the body and brain employ complex autonomous networks to accomplish every action we perform. These networks are managed by mechanisms that link our intentions, actions, and their sensory outcomes. Together, this create a sense of embodiment; the perception that our body and actions are indeed our own. Most autonomous machines do not communicate or behave in ways that establish these same links. Operators are acutely aware that a machine’s cooperative actions are not their own, and our natural human tendencies to become frustrated with and blame machines when errors occur can promote the abandonment of these same technologies that promise to extend our capabilities. This award supports fundamental research to characterize the relationships between frustration, blame and the embodiment of autonomous machines. It has implications in driving our willingness to cooperate with error-prone autonomous systems across a growing number of applications including prostheses, powered exoskeletons, and other robotic technologies designed to augment human capabilities. This work will provide interdisciplinary research opportunities for underrepresented groups as well as positively impact engineering and neuroscience education. A unique human neuro-robotic model in which participants with upper limb amputation and targeted reinnervation surgery (a neural-machine interface) pilot cooperative robotic limbs will be used. Here, participants can operate artificial limbs by thinking about moving their missing limbs while also feeling movement and touch. It has been previously demonstrated that these sensorimotor channels can be manipulated to promote the embodiment of prostheses. Using this model, it will be investigated how embodiment can be promoted using touch and movement sensory feedback when operating robotic limbs with varying degrees of autonomy and in the face of deteriorating control. Additionally, using cohorts of able-bodied participants and amputees with targeted reinnervation surgery, a robust data set will be built that links measures of embodiment to user frustration and blame while performing cooperative tasks with virtual human-like and non-anthropomorphic robotic limbs. From this data regression modelling techniques will be applied to develop a quantitative index that links the severity of blame and user frustration to the degree of embodiment of cooperative machines.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.

该奖项将提供一个新的框架，通过模糊机器，操作员和他们的教练行动之间的界限来促进人类与自动驾驶机器之间的更宽容的关系。作为人类，我们有效地将自主系统用作身体和大脑员工复杂的自主网络来完成我们执行的所有动作。这些网络是通过将我们的意图，动作及其感觉结果联系起来的机制来管理的。这共同创造了一种体现感。对我们的身体和行为的看法确实是我们自己的。大多数自主机不会以建立这些相同链接的方式进行交流或行为。操作员敏锐地意识到，机器的合作行动不是他们自己的，当出现错误时，我们的自然人类倾向于对机器感到沮丧和模糊机器，可以促进放弃这些相同的技术，这些技术有望扩大我们的能力。该奖项支持基本研究，以表征挫败感，模糊与自动驾驶机器体现之间的关系。它具有推动我们愿意与越来越多的应用程序的易用自主系统进行协调的意愿，包括假体，动力外骨骼和其他旨在增强人类能力的机器人技术。这项工作将为代表性不足的群体提供跨学科的研究机会，并积极影响工程和神经科学教育。一种独特的人类神经动物模型，其中将使用具有上肢截肢和靶向重新连接手术（神经机界面）的参与者。在这里，参与者可以通过思考移动缺失的四肢，同时感到运动和触摸来操纵人造四肢。以前已经证明，可以操纵这些感觉运动通道以促进假体的实施。使用此模型，将研究如何使用触摸和运动感觉反馈来促进实施方案，当时具有不同程度的自主权并面对确定控制的机器人四肢操作机器人四肢。此外，将使用具有针对性的重新支配手术的大量健全的参与者和截肢者，将建立一个可靠的数据集，以将实施例的度量与用户挫败感和模糊联系在一起，同时执行合作任务与虚拟人类和非人性化和非人性化机器人的机器人四肢。通过此数据回归建模技术将应用于开发一个定量指数，将模糊和用户挫败感与合作机器的体现程度联系起来。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来获得的支持。