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.

在智能系统为与人一起提供帮助或工作的应用中，人类机器人的互动越来越重要。该研究计划的最终目标是开发针对智能系统设计的新型生物学启发的机制，使智能，自主机器人能够与联合任务中的人们互动，以促进共同学习和共同适应。在该计划的接下来的5年中，特定目标是a）开发适当的共进计算机制，以实时学习和适应可信赖的机器人，b）在建立，维持和维护这些因素中，可以使人类易于互动的机器人的机制来确定，维持和​​维修的机器人，以研究机器人实现，错误和其他因素的影响。并传达（例如，可视化）他们对自己的学习和决策的能力和信心水平，以改善人类机器人的信任，d）开发计算，行为模型，以根据机器人对社交提示和互动方式创建值得信赖的机器人行为。对体系结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。预计这项工作的结果会改变社会和聪明的自主机器人与人互动的方式。从方法上讲，我的工作正在使用人工智能，人类计算机和人类机器人相互作用的各种方法。该研究计划的新颖性是从人类/动物行为中汲取灵感，然后为社会和智能机器人设计计算模型。与对人类/ android机器人的共同关注相比，沟通和互动的生物学模型更普遍，更容易理解，这可以产生更强大，更有效的交流手段，并在不同的机器人体现上更好地跨越不同的机器人实施例，而对人类/ android机器人的共同关注，这些机器人在系统的能力方面引起了人们的高度期望，但通常会导致人们失望的是未达到预期的情况。该程序的成果将大大推动计算机科学和工程的发展，以创建人们可以自然地并有效地与之互动的机器人系统，并且他们会信任。该项目将培训HQP，以开发基本解决方案，以开发可信赖，社会和聪明的机器人。它将大大推动学生在学术界或行业未来职业所需的跨学科知识和技能，在这种情况下，项目结果可能会改变在现实世界应用领域中的这种系统的使用，例如加拿大的护理或服务部门。