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.

能够通过口语与人交流的机器人将推动人类工作的未来，并帮助社会中最弱势的成员，包括儿童和老年人、残疾人、自闭症或精神疾病患者，以及遭受孤立、欺凌、或外伤。机器人在与日常人交谈时需要完成的关键任务之一是指称表达生成，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，它们需要以准确的（描述不应该是错误的）、自然的（描述不应该听起来很尴尬）、易于理解的（听者应该能够快速理解描述）的方式来进行。并且毫不费力），高效（机器人应该能够生成描述，而不必暂停和思考太久）。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是如何这样做的。我们擅长生成指称表达的原因之一可能是因为我们的工作记忆或短期记忆，我们用它来以一种我们可以快速、轻松访问的方式保留少量及时且重要的信息。该项目的关键思想是为机器人提供相同类型的工作记忆能力，并以相同的方式思考人类工作记忆中可能存在的内容，这样它们就能够利用这些及时且重要的信息来做得更好。生成引用表达式的工作。通过采用这种受认知启发的方法，这项工作将推进多个领域的最新技术，包括人工智能、机器人和心理学。此外，该项目的教育方面旨在开发材料，帮助培训在这些领域交叉领域工作的下一代学生。为了确保产生尽可能广泛的影响，这些努力将与 PI 部门有关扩大计算参与的活动相结合，以便覆盖目前代表性不足的群体。从技术角度来看，这项研究的主要目标是展示工作记忆模型如何适当地缓存目标相关对象的任务相关信念，从而使机器人能够更好地执行指代表达生成。为此，这项工作将评估两个关键假设：受认知启发的工作记忆模型将使机器人能够以更准确、自然、计算效率更高的方式生成指称表达，并且对于听者来说处理认知效率更高；并且可以利用目标相关性来确保在这些模型中保留与任务最相关的信息。通过解决这些假设，该研究将开发：（1）第一个在机器人认知架构中生成指代表达的算法，该算法以当前人类工作记忆的心理学理论为基础； （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有一个基本的新理解，（3）从机器人和认知建模的角度理解哪些记忆模型将产生最佳性能； （4）对如何在集成认知架构中自动评估实体及其属性的目标相关性的基本新理解； (5) 了解如何利用目标相关性在机器人工作记忆模型中分配认知资源； （6）从机器人和认知建模的角度了解哪些目标驱动的资源分配策略将产生最佳性能； (7) 免费提供的人机对话数据集，以及一个免费提供的实验框架，允许其他研究人员收集更多此类对话，这两者都将通过开放科学框架永久存档。该奖项反映了 NSF 的法定使命通过使用基金会的智力价值和更广泛的影响审查标准进行评估，并被认为值得支持。

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