Developing Technologies for Regulating Robot Persuasion in Human-Robot Interaction

开发在人机交互中调节机器人说服的技术

• 批准号: RGPIN-2016-05994
• 负责人: Young, James
• 金额: $ 2.26万
• 依托单位: University of Manitoba
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616036
• 关键词: Developing; Technologies; Regulating; Robot; Persuasion

Robots are emerging as viable tools in domains including personal care, education, and search and rescue, where they increasingly work alongside people as autonomous or semi-autonomous tools and assistants. In addition to the difficult robotics engineering challenges, robots that enter people's spaces must also have effective interface technologies that support human-robot interaction and enable the general public - not only robotics experts - to work with robots, give commands, and to get data from them. Robots collocated with people use human-like social techniques such as gestures or gaze, leveraging people's existing inter-personal skills to simplify interaction. These social robots will often be in positions where they offer recommendations to people, and research has shown how expert-reference robots can be more persuasive than anticipated, in part due to people's trust in robotic memory, knowledge, and computational ability. This can be dangerous, for example a healthcare provider may doubt their assessment when contradicted by a specialized healthcare robot, but can also be desirable, for example a persuasive rehabilitation robot could help with recovery exercises. Research has established that robots can be persuasive, but little is known about how to practically create a robot to interact with people to either avoid or encourage persuasion. In the short term, I propose to develop the first interaction framework and technologies that demonstrate how robots can modify their persuasiveness by changing how they interact with people, specifically targeting how a robot can use its body language. In the long term, my program will help build a solid understanding and array of tools for programming robots to interact effectively and appropriately with people. My program will methodologically investigate how robots can use their physical body to impact persuasion; specifically, how robots can use their gestures, posture, gaze, and use of interpersonal space, to increase or decrease persuasiveness. I will implement and formally evaluate original interface technologies and algorithms for enabling robots to leverage these techniques for interaction. For this I will establish the first methodologies and metrics for measuring and comparing persuasiveness of robotic designs, and conduct a series of experiments to develop interface design frameworks for engineering robot persuasion. This work will provide foundations for understanding how nuanced robotic behaviors can impact persuasion, including persuasion-oriented interface guidelines, and some of the first interaction techniques and algorithms for architecting robotic persuasion. Ultimately, this will be important for enabling human-robot interaction programmers to be in control of their robots: to reduce the persuasiveness of robots in potentially dangerous situations, or make robots more convincing when appropriate.

机器人正在成为个人护理、教育、搜索和救援等领域的可行工具，它们越来越多地作为自主或半自主工具和助手与人类一起工作。除了困难的机器人工程挑战外，进入人类空间的机器人还必须具有有效的接口技术，以支持人机交互，并使普通公众（不仅是机器人专家）能够与机器人一起工作，发出命令并从机器人中获取数据。 与人类搭配的机器人使用类似人类的社交技术，如手势或凝视，利用人们现有的人际交往技能来简化互动。这些社交机器人通常会处于向人们提供建议的位置，研究表明专家参考机器人比预期更有说服力，部分原因是人们对机器人记忆，知识和计算能力的信任。这可能是危险的，例如，医疗保健提供者在与专业医疗保健机器人相矛盾时可能会怀疑他们的评估，但也可能是可取的，例如，有说服力的康复机器人可以帮助恢复练习。研究已经证实，机器人可以具有说服力，但人们对如何实际创造一个机器人与人互动以避免或鼓励说服知之甚少。在短期内，我建议开发第一个交互框架和技术，展示机器人如何通过改变与人的交互方式来改变其说服力，特别是针对机器人如何使用其肢体语言。从长远来看，我的计划将有助于建立一个坚实的理解和编程机器人的工具阵列，以有效和适当地与人互动。 我的计划将从方法论上研究机器人如何使用他们的身体来影响说服力;具体来说，机器人如何使用他们的手势，姿势，凝视和人际空间的使用来增加或减少说服力。我将实现并正式评估原始的接口技术和算法，使机器人能够利用这些技术进行交互。为此，我将建立测量和比较机器人设计的说服力的第一种方法和指标，并进行一系列实验，以开发工程机器人说服力的界面设计框架。 这项工作将为理解细微差别的机器人行为如何影响说服提供基础，包括面向说服的界面指南，以及一些用于构建机器人说服的第一批交互技术和算法。最终，这对于使人机交互程序员能够控制他们的机器人非常重要：降低机器人在潜在危险情况下的说服力，或者在适当的时候使机器人更具说服力。