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

在智能系统为人类提供帮助或与人类一起工作的应用中，人机交互越来越重要。 该研究计划的最终目标是开发用于智能系统设计的新型生物启发机制，使智能自主机器人能够与人类参与联合任务，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）为可信赖的机器人开发适当的共同学习计算机制，这些机器人可以实时学习和适应，B）研究机器人实施，错误和其他相关因素对建立，维护和修复人机信任的影响，并将这些因素整合到能够维持长期人机交互的计算架构中，c）为机器人设计计算机制，（例如，可视化）他们在自己的学习和决策中的能力和信心水平，以提高人机信任，以及d）开发计算的行为模型，用于在机器人使用社会线索和交互模式方面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。这项工作的成果有望改变社交和智能自主机器人与人互动的方式。在方法论上，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是从人类/动物行为中获得灵感，然后为社会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且更容易理解，这可以产生更鲁棒和有效的通信手段，并且与共同关注人形/机器人机器人相比，更好地在不同的机器人实施例中推广，人形/机器人机器人在系统的能力方面唤起人们的高期望，但是当期望未被满足时通常导致失望。该计划的成果将大大推进计算机科学和工程，以创造人们可以“自然”和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开发基础解决方案，以开发可信赖的，社交和智能机器人。它将大大提高学生未来在学术界或工业界的职业生涯所需的跨学科知识和技能，项目结果可能会改变这些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。

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.