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关系框架，通过模糊机器、操作员及其合作行为之间的界限，促进人类和自主机器之间更加宽容。作为人类，我们天生能够有效地使用自主系统，因为身体和大脑都采用复杂的系统。这些网络由将我们的意图、行动及其感官结果联系起来的机制管理，这创造了一种体现感，即我们的身体和行动确实是我们自己的。机器不会以建立这些相同链接的方式进行通信或行为。操作员敏锐地意识到，机器的合作行为不是他们自己的，当错误发生时，我们人类自然倾向于对机器感到沮丧和责怪，这可能会促使我们放弃那些有望扩展我们能力的技术。该奖项支持基础研究它描述了挫败感、责备和自主机器的体现之间的关系，这对于推动我们在越来越多的应用中与容易出错的自主系统进行合作具有重要意义，这些应用包括假肢、动力外骨骼和其他旨在增强人类能力的机器人技术。 .这项工作将提供为代表性不足的群体提供跨学科研究机会，并对工程和神经科学教育产生积极影响。这里将使用上肢截肢和有针对性的神经再支配手术（神经机器接口）试点合作机器人肢体的参与者。参与者可以通过思考移动他们缺失的肢体，同时感受运动和触觉来操作假肢。之前已经证明，可以操纵这些感觉运动通道来促进假肢的具体化，我们将研究如何使用该模型。当操作具有不同程度自主性的机器人肢体时，以及面对控制力下降的情况，可以使用触摸和运动感觉反馈来促进身体的改善。此外，通过使用健全的参与者和截肢者群体进行有针对性的神经再支配手术，将建立一个强大的数据集。在与虚拟类人和非拟人机器人肢体执行合作任务时，将体现的测量与用户的挫败感和责备联系起来。根据该数据，回归建模技术将用于开发将责备的严重性和用户挫败感联系起来的定量指数。学位该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。