Biologically Inspired Robotics: Intelligent Systems for Trustworthy Human-Robot Co-learning and Adaptation

仿生机器人：可信赖的人机协同学习和适应的智能系统

• 批准号: RGPIN-2019-05223
• 负责人: Dautenhahn, Kerstin
• 金额: $ 3.5万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=751579
• 关键词: Biologically; Inspired; Robotics; Intelligent; Systems

Human-robot interactions are increasingly important in applications where intelligent systems provide assistance to or work alongside people. The ultimate goal of this research program is to develop novel biologically-inspired mechanisms for intelligent system design, enabling intelligent, autonomous robots to engage with people in joint-tasks facilitating co-learning and co-adaptation. Specific objectives over the next 5 years of this program are a) the development of appropriate co-learning computational mechanisms for trustworthy robots that learn and adapt in real-time, b) the study of the impact of robot embodiment, errors, and other factors relevant in establishing, maintaining and repairing human-robot trust, and integrating those factors into computational architectures that can sustain long-term human-robot interaction, c) design of computational mechanisms for robots that can assess and convey (e.g. visualise) their level of competence and confidence in their own learning and decision making, in order to improve human-robot trust, and d) to develop computational, behavioural models for creating trustworthy robot behaviour in terms of the robot's use of social cues and interaction modalities. Experimental evaluation of the architectures and algorithms will involve technical evaluation as well as validation in user studies. Outcomes of this work are expected to change the way social and intelligent autonomous robots interact with people. Methodologically, my work is using diverse methods from Artificial Intelligence, Human-Computer and Human-Robot Interaction. A novelty of this research program is to take inspiration from human/animal behaviour and then design computational models for social and intelligent robots. Biological models of communication and interaction are more universally and readily understood, which can yield more robust and efficient means of communication, and to generalize better across different robot embodiments, compared to the common focus on humanoid/ android robots that evoke high expectations in people in terms of the system's capabilities, but often lead to disappointment when expectations are not met. Outcomes of this program will significantly advance computer science and engineering towards creating robotic systems that people can "naturally" and efficiently interact with, and that they will trust. The project will train HQP in developing fundamental solutions to the development of trustworthy, social and intelligent robots. It will significantly advance the students' interdisciplinary knowledge and skills needed for a future career in academia or industry, where project results can potentially transform the use of such systems in real-world application areas, e.g. the care or service sectors in Canada.

在智能系统向人提供帮助或与人一起工作的应用中，人与机器人的交互变得越来越重要。这项研究计划的最终目标是开发新颖的生物启发机制，用于智能系统设计，使智能、自主的机器人能够与人类进行联合任务，促进共同学习和共同适应。该计划未来5年的具体目标是a)为值得信赖的机器人开发适当的实时学习和适应的协同学习计算机制，b)研究建立、维护和修复人-机器人信任中的机器人体现、错误和其他相关因素的影响，并将这些因素整合到能够维持长期人-机器人交互的计算体系结构中，c)设计机器人的计算机制，该计算机制可以评估和传达(例如，可视化)它们在自己的学习和决策中的能力和信心水平，以便改善人-机器人信任，以及d)开发计算、根据机器人对社交线索和交互模式的使用，创建可信赖的机器人行为的行为模型。架构和算法的实验评估将包括技术评估以及用户研究中的验证。这项工作的成果有望改变社交和智能自主机器人与人互动的方式。在方法上，我的工作是使用人工智能、人机和人机交互的各种方法。这项研究计划的一个新奇之处是从人类/动物行为中获得灵感，然后为社交和智能机器人设计计算模型。通信和交互的生物模型更普遍和更容易理解，这可以产生更健壮和更有效的通信手段，并更好地在不同的机器人实施例中推广，而不是通常关注类人/安卓机器人，后者在系统能力方面引起人们的高期望，但在期望未达到时往往导致失望。这一计划的成果将极大地推动计算机科学和工程技术，创造出人们能够“自然”地、高效地与之互动并值得信任的机器人系统。该项目将培训HQP开发可信赖的、社交的和智能机器人开发的基本解决方案。它将显著提高学生未来在学术界或工业领域所需的跨学科知识和技能，在这些领域，项目成果可能会改变此类系统在现实世界应用领域的使用，例如加拿大的护理或服务部门。