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

该奖项将提供一个新的框架，通过模糊机器、操作员和他们的合作行为之间的界限，促进人类和自动机器之间更宽容的关系。作为人类，我们天生就能有效地使用自主系统，因为身体和大脑使用复杂的自主网络来完成我们所做的每一个动作。这些网络是由连接我们的意图、行动及其感官结果的机制来管理的。总之，这创造了一种化身感；我们的身体和行为确实是我们自己的感知。大多数自主机器不会以建立这些相同联系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，当机器出现错误时，我们人类的自然倾向会对机器感到沮丧和指责，这可能会促使我们放弃这些承诺扩展我们能力的技术。该奖项支持基础研究，以表征挫折、指责和自主机器的体现之间的关系。在越来越多的应用中，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，它在推动我们愿意与容易出错的自主系统合作方面具有重要意义。这项工作将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。一种独特的人类神经-机器人模型，其中参与者上肢截肢和定向再神经手术（神经-机器接口）试点合作机器人四肢将被使用。在这里，参与者可以通过想象移动他们失去的肢体，同时感受运动和触摸来操作假肢。以前已经证明，这些感觉运动通道可以被操纵来促进假体的具体化。利用该模型，研究如何在不同程度的自主操作机械肢体时，在控制能力恶化的情况下，利用触觉和运动感觉反馈来促进体现。此外，使用健全的参与者和有针对性的神经移植手术截肢者的队列，将建立一个强大的数据集，将体现的措施与用户沮丧和责备联系起来，同时使用虚拟人形和非拟人化的机器人肢体执行合作任务。根据这些数据，回归建模技术将被应用于开发一种定量指数，该指数将责备和用户挫折的严重程度与合作机器的体现程度联系起来。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.