Intentional Vision in Humans and Robots

这个项目将探讨人们如何理解机器人的表征状态，以及这种认知的认知和感知基础，特别是关于视觉。具体来说，一系列的实验研究将探索人们对一个名为ISAC的高度拟人化的人形机器人的信念。最基本的实验将询问受试者是否高估了ISAC看到视觉变化的能力。后续研究将探讨这些误解影响假设的程度，这些假设可能是在线人机交互的基础，本研究将探讨调用拟人化模型的感知基础。人们对视觉体验做出系统性的错误预测，大大高估了自己和他人看到视觉变化的能力，而且，这些高估也适用于机械表征系统，如计算机，当它们被描述为具有拟人化的信念，目标和意图时。研究也开始确定具体的知觉线索，可能有助于引导知识的表征。该项目的重点是理解机器人系统的人类用户的意图视觉，并最终将这种理解作为改善机器人处理人类用户意图的人工智能（AI）的基础。这项研究代表了从认知发展中获得的见解的一种新的应用，以了解成年人如何模仿机械表征系统。因此，它不仅有可能促进我们对成人表征模型和这些模型的感知基础的理解，而且有可能指导人工智能底层人机交互的发展。特别是，这项研究将隔离类人机器人的用户模型可能与现实不同的情况，并为人工智能编程指定一个生态有效的基础，可以构建有意的人类行为的编码。 这项研究不仅将丰富范德比尔特大学认知科学和工程界之间的现有合作，而且还将产生更广泛的教育影响。在类人机器人的背景下测试这些想法也将为研究生和本科生提供一个令人信服的背景，以考虑表征和心灵的基本问题，预计范德比尔特大学的本科生将在协助这项研究中发挥至关重要的作用。目前，人形机器人正在开发中，以填补现实世界的功能，从家务到照顾老人。在这些设备带来的挑战中，最困难的可能是机器人和人类用户之间需要双向理解。不仅人类需要理解机器人的能力和表征状态，机器人也需要对人类有同样的理解。如果机器人要与人类进行富有成效和灵活的互动，这一点尤其如此，这一过程需要仔细调整理解，这种理解是动态的，足以协调不断变化的环境、信念、愿望和意图的复杂流动。 这项研究将加强改善人机交互的科学基础。

This project will explore how people construe the representational states of robots, and the cognitive and perceptual basis for this construal, particularly with respect to vision. Specifically, a series of experimental studies will explore people's beliefs about a highly anthropomorphic humanoid robot named ISAC. The most basic experiments will ask whether subjects overestimate ISAC's ability to see visual changes. Follow-ups will explore the degree to which these misunderstandings affect assumptions that might underlie on-line human-robot interactions, and this research will explore the perceptual basis for invoking an anthropomorphic model. People make systematic mispredictions about visual experience, vastly overestimating their own and others' ability to see visual changes, and further, these overestimates also apply to mechanical representational systems such as computers when they are described as having anthropomorphic beliefs, goals, and intentions. Research has also begun to identify specific perceptual cues that may serve to bootstrap knowledge about representations. The focus of this project is both to understand intentional vision in the human users of robotic systems, and ultimately to use this understanding as the basis for improving the artificial intelligence (AI) underlying the robots' processing of human users' intentions. The research represents a novel application of insights gained from cognitive development to understanding how adults construe mechanical representational systems. As such, it not only has the potential to advance our understanding of adults' models of representation and the perceptual basis of these models, but it also has the potential to guide the development of the AI underlying human-robot interaction. In particular, this research will isolate situations in which user models of humanoid robots may diverge from reality, and specify an ecologically valid basis for AI programming that can structure the coding of intentional human action. This research will not only enrich the existing collaborations between the cognitive science and engineering communities at Vanderbilt University, but it will also have a broader educational impact. Testing these ideas in the context of a humanoid robot will also provide a compelling context for both graduate and undergraduate students to consider basic questions of representation and mind, and it is expected that Vanderbilt undergraduates will play a crucial role in assisting with this research. Humanoid robots are currently being developed to fill real-world functions ranging from household chores to elder-care. Among the challenges these devices pose, perhaps the most difficult is the need for a two-way understanding between the robots and their human users. Not only do humans need to understand robot capabilities and representational states, but robots require the same understanding of humans. This is particularly true if robots are to have productive and flexible interactions with humans, a process that requires a careful alignment of understanding that is dynamic enough to coordinate a complex flow of changing circumstances, beliefs, desires, and intentions. This research will strengthen the scientific basis for efforts to improve human-robot interaction.