CAREER: Cognitively-Informed Memory Models for Language-Capable Robots

职业：具有语言能力的机器人的认知信息记忆模型

• 批准号: 2044865
• 负责人: Thomas Williams
• 金额: $ 55万
• 依托单位: Colorado School of Mines
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-15 至 2026-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2044865&HistoricalAwards=false
• 关键词: CAREER; Cognitively; Informed; Memory; Models

Robots that can communicate with people through spoken language stand to advance the future of human work and to assist the most vulnerable members of society, including children and older adults, people with disabilities, autism, or mental illness, and people experiencing isolation, bullying, or trauma. One of the key tasks that robots will need to do when talking with everyday people is referring expression generation, which is the process of creating descriptions like "the office at the end of the hallway." When robots generate such descriptions, they need to do so in a way that is accurate (the description shouldn't be wrong), natural (the description shouldn't sound awkward), understandable (the listener should be able to interpret the description quickly and effortlessly), and efficient (the robot should be able to generate the description without having to pause and think for too long). To understand how robots might generate descriptions in a way that satisfies these properties, we can start by trying to understand how people do so. One reason we are good at generating referring expressions may be because of our working, or short term, memory, which we use to keep a small amount of timely and important information available in a way that we can quickly and effortlessly access. The key idea of this project is to give robots the same type of working memory capabilities, and the same ways of thinking about what might be in peoples' working memories, so they will be able to use that timely and important information to do a better job at generating referring expressions. By taking this cognitively inspired approach, this work will advance the state of the art of multiple fields, including AI, robotics, and psychology. In addition, the educational aspect of this project aims to develop materials that will help train the next generation of students working at the intersection of these fields. To ensure the broadest possible impact, these efforts will be integrated with the PI's department's activities relating to Broadening Participation in Computing so that they reach currently underrepresented groups. From a technical perspective, the key goal of this research is to show how models of working memory that appropriately cache task-relevant beliefs about goal-relevant objects will enable robots to better perform referring expression generation. To this end, the work will assess two key hypotheses: that cognitively inspired models of working memory will enable robots to generate referring expressions in a way that is more accurate, natural, computationally efficient to generate, and cognitively efficient for the listener to process; and that goal relevance can be leveraged to ensure that the most task-relevant information is retained within those models. By addressing these hypotheses, the research will develop: (1) the first algorithms for referring expression generation in robot cognitive architectures that are informed by current psychological theories of human working memory; (2) a fundamental new understanding of how robots can intelligently manage and allocate resources within artificial working memory models, (3) an understanding of which memory models will produce optimal performance from both robotics and cognitive modeling perspectives; (4) fundamental new understanding of how the goal relevance of entities and their properties can be automatically assessed within integrated cognitive architectures; (5) understanding of how goal relevance can be used to allocate cognitive resources within robotic models of working memory; (6) understanding of which goal-driven resource allocation strategies will produce optimal performance from both robotics and cognitive modeling perspectives; and (7) freely-available datasets of human-robot dialogues, and a freely-available experimental framework to allow other researchers to collect additional such dialogues, both of which will be permanently archived via the Open Science Framework.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.

可以通过口语与人们交流的机器人可以促进人类工作的未来，并协助社会上最脆弱的成员，包括儿童和老年人，残疾人，自闭症或精神疾病，以及经历孤立，欺凌或创伤的人们。机器人与日常人们交谈时需要做的关键任务之一是指代表达生成，这是创建诸如“走廊尽头的办公室”之类的描述的过程。当机器人生成这样的描述时，他们需要以准确的方式（描述不应该是错误的），自然（描述不应该听起来很尴尬），可以理解的（听众应该能够快速又毫不费力地解释描述），并且有效地解释了描述（机器人应该能够生成描述而无需停止并思考太久）。为了了解机器人如何以满足这些属性的方式生成描述，我们可以首先尝试了解人们如何做到这一点。我们善于生成参考表达式的原因之一可能是由于我们的工作或短期内存，我们用来以我们可以快速而轻松地访问的方式保留少量及时和重要的信息。该项目的关键思想是为机器人提供相同类型的工作记忆功能，并以相同的思考方式来思考人们的工作记忆中可能是什么，以便他们能够使用及时和重要的信息来更好地生成推荐表达式。通过采用这种认知灵感的方法，这项工作将推动包括AI，机器人技术和心理学在内的多个领域的艺术状态。此外，该项目的教育方面旨在开发材料，以帮助培训在这些领域交叉点工作的下一代学生。为了确保最广泛的影响，这些努力将与PI部门的活动有关，该活动与扩大计算的活动有关，以使它们达到目前代表性不足的群体。从技术角度来看，这项研究的关键目标是展示如何适当缓存与任务相关的目标对象的工作记忆模型如何使机器人能够更好地执行参考表达生成。为此，工作将评估两个关键的假设：认知灵感的工作记忆模型将使机器人能够以更准确，自然，在计算上有效产生的方式生成转介表达式，并有效地为听众进行处理；并且可以利用该目标相关性，以确保将最重要的信息保留在这些模型中。通过解决这些假设，研究将发展：（1）引用在机器人认知体系结构中产生表达产生的算法，这些算法由当前人类工作记忆的当前心理学理论所告知； （2）对机器人如何在人工工作记忆模型中智能管理和分配资源的基本新理解，（3）对哪些内存模型将从机器人和认知建模的角度产生最佳性能的理解； （4）对实体及其属性的目标相关性如何自动评估的基本新理解； （5）了解如何使用目标相关性来分配工作记忆的机器人模型中的认知资源； （6）了解哪种目标驱动的资源分配策略将从机器人和认知建模的角度产生最佳性能； （7）人类机器人对话的可自由可用数据集，以及一个可以自由获得的实验框架，允许其他研究人员收集其他此类对话，这两个对话都将通过开放的科学框架永久存档。本奖奖反映了NSF的法定任务，并通过使用基金会的智力效果进行评估，并被评估值得评估。

项目成果

The Eye of the Robot Beholder: Ethical Risks of Representation, Recognition, and Reasoning over Identity Characteristics in Human-Robot Interaction

机器人旁观者之眼：人机交互中身份特征的表示、识别和推理的道德风险

• DOI: 10.1145/3568294.3580031
• 发表时间: 2023
• 期刊: Human-Robot Interaction
• 作者: Williams, Tom

Rube-Goldberg Machines, Transparent Technology, and the Morally Competent Robot

鲁布-戈德堡机器、透明技术和有道德能力的机器人

• DOI: 10.1145/3568294.3580163
• 发表时间: 2023
• 期刊: ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Mott, Terran;Williams, Tom
• 通讯作者: Williams, Tom

The Importance of Memory for Language-Capable Robots

记忆对于具有语言能力的机器人的重要性

• DOI: 10.1145/3611687
• 发表时间: 2023
• 期刊: The ACM Magazine for Students
• 作者: Silva, Rafael Sousa;Han, Zhao;Williams, Tom
• 通讯作者: Williams, Tom

Enabling Human-like Language-Capable Robots Through Working Memory Modeling

通过工作记忆建模实现具有类人语言能力的机器人

• DOI: 10.1145/3568294.3579967
• 发表时间: 2023
• 期刊: ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Sousa Silva, Rafael;Williams, Tom
• 通讯作者: Williams, Tom

Community Futures With Morally Capable Robotic Technology

具有道德能力的机器人技术的社区未来

• 发表时间: 2023
• 期刊: HRI Workshop on Perspectives on Moral Agency in Human-Robot Interaction
• 作者: Mott, Terran;Williams, Tom
• 通讯作者: Williams, Tom