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: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738131
• 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.

在智能系统为人类提供帮助或与人类一起工作的应用中，人机交互变得越来越重要。该研究计划的最终目标是为智能系统设计开发新颖的受生物学启发的机制，使智能自主机器人能够与人类共同执行任务，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）为可实时学习和适应的值得信赖的机器人开发适当的共同学习计算机制，b）研究机器人实施例、错误以及与建立、维护和修复人机信任相关的其他因素的影响，并将这些因素集成到可以维持长期人机交互的计算架构中，c）为可以评估的机器人设计计算机制 并传达（例如可视化）他们对自己的学习和决策的能力水平和信心，以提高人机信任，以及 d) 开发计算行为模型，根据机器人对社交线索和交互方式的使用来创建值得信赖的机器人行为。架构和算法的实验评估将涉及技术评估以及用户研究中的验证。这项工作的成果预计将改变社交和智能自主机器人与人类互动的方式。从方法论上来说，我的工作使用了人工智能、人机交互和人机交互等多种方法。该研究项目的新颖之处在于从人类/动物行为中获取灵感，然后设计社交和智能机器人的计算模型。与人们对人形/机器人机器人的共同关注相比，通信和交互的生物模型更加普遍和容易理解，这可以产生更强大和更有效的通信方式，并且可以更好地推广到不同的机器人实施例，人形/机器人机器人在系统功能方面引起人们的高期望，但当期望未得到满足时往往会导致失望。该计划的成果将显着推进计算机科学和工程，以创建人们可以“自然”且有效地交互并且值得信赖的机器人系统。该项目将培训 HQP 开发基本解决方案，以开发值得信赖的社交智能机器人。它将显着提高学生未来在学术界或工业界的职业所需的跨学科知识和技能，其中项目结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。