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动之间的界限，促进人类和自主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件事。这些网络由连接我们的意图、行动及其感官结果的机制管理。总而言之，这创造了一种化身的感觉；感觉到我们的身体和行动确实是我们自己的。大多数自主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，机器的合作行动不是他们自己的，我们人类在发生错误时对机器感到沮丧并责怪机器的自然倾向，可能会促使这些承诺扩展我们能力的技术被抛弃。该奖项支持基础研究，以刻画挫折、指责和自主机器化身之间的关系。它对推动我们在越来越多的应用中与容易出错的自主系统合作的意愿产生了影响，这些应用包括假肢、电动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，并对工程学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，在该模型中，参与者将使用上肢截肢和定向神经再支配手术(神经-机器接口)试点合作机器人肢体。在这里，参与者可以通过思考移动他们缺失的肢体来操作假肢，同时还可以感觉到运动和触摸。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体现。利用该模型，将研究在操作具有不同程度自主性的机械臂时，以及在控制恶化的情况下，如何利用触摸和运动感觉反馈来促进具体化。此外，利用接受定向神经再生手术的健全参与者和截肢者的队列，将建立一个稳健的数据集，将具体化措施与用户在使用虚拟人类和非拟人化机械臂执行合作任务时的挫折感和责备联系起来。根据这些数据，回归建模技术将被应用来开发一个量化指数，该指数将指责的严重性和用户的挫折感与合作机制的体现程度联系起来。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.