Facilitating Bidirectional Communication for Fast and Flexible Human-Robot Collaboration

协作机器人可以通过将机器精度与人类灵活性相结合来改善和增强制造业和医疗保健等各个行业的运营。然而，在开发智能机器人助手方面仍然存在巨大的挑战，这些智能机器人助手可以在结构化程度较低的环境中与人类同行一起灵活，安全和智能地操作;在理解机器人如何导航和弥合机器与人类之间的交互裂缝方面存在差距。为了实现流畅的协作，必须开发方法和算法来增强机器人和人类之间的理解、学习和沟通。我提出的研究旨在通过四种方法解决人机交互（HRI）环境中更紧密的通信渠道的需求：（a）使用机器学习对人类行为和不可预测性进行建模，以将机器人响应置于情境中，（B）探索社交和物理HRI中的双向非语言交流线索，以支持流畅的协作，（c）制定策略，使机器人能够随着时间的推移学习改善行为，以及（d）使用虚拟现实（VR）和增强现实（AR）来增强用户对机器人环境和行为的理解。这项工作旨在为智能协作机器人助手走出实验室，进入家庭和社区铺平道路，在那里他们可以安全有效地支持和帮助人们。我希望通过这个研究项目培养三名本科生，三名硕士生和两名博士生。这些HQP将在HRI，机器学习和AR/VR的前沿领域提供实践经验，传授行业和学术界所追求的相关技能和知识。

Collaborative robotics can improve and enhance operations in various industries, such as manufacturing and healthcare, by merging machine precision with human flexibility. However, there are still tremendous challenges in developing intelligent robot assistants that can operate in less-structured contexts alongside human counterparts flexibly, safely, and intelligently; gaps exist in understanding how robots are supposed to navigate and bridge the interaction rift between machines and humans. For fluid collaboration, methods and algorithms must be developed to enhance understanding, learning, and communication between robots and humans. My proposed research aims to address the need for tighter communication channels in human-robot interaction (HRI) contexts through four methods: (a) modelling of human behaviour and unpredictability to contextualize robot responses using machine learning, (b) exploring bidirectional non-verbal communicative cues in both social and physical HRI to support fluent collaboration, (c) developing strategies to allow robots to learn to improve behaviours over time, and (d) using virtual reality (VR) and augmented reality (AR) to enhance user understanding of robot contexts and behaviours. This work aims to pave the way for intelligent collaborative robotic assistants to move out of the lab and into homes and workspaces where they can support and assist people safely and effectively. I expect to train three undergraduate, three master's, and two doctoral students through this research program. These HQP will be provided with hands-on experience in the cutting-edge domains of HRI, machine learning, and AR/VR, imparting relevant skills and knowledge sought after by industry and academia.