Mukherjee Debasmita, Gupta Kashish, Najjaran Homayoun
Advanced Control and Intelligent Systems Laboratory, School of Engineering, The University of British Columbia, Kelowna, BC, Canada.
Advanced Control and Intelligent Systems Laboratory, Faculty of Engineering, University of Victoria, Victoria, BC, Canada.
Front Robot AI. 2022 Jul 11;9:870477. doi: 10.3389/frobt.2022.870477. eCollection 2022.
Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the "naturalness" inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.
人机通信是一个正在积极研究的领域，旨在实现人类与智能工业机器人系统之间高效、无缝的协作。该领域起源于人类通信，旨在实现后者所固有的“自然性”。工业人机通信追求通过简单的命令和手势进行通信，这并不代表不受控制的现实世界工业环境。此外，通信中的自然性是其动态性的结果，而在工业人机通信中，动态性通常被视为一种设计标准而被忽视。基于复杂性理论的自然通信模型能够更准确地表示人类通信，经过调整后，也能使机器人通信领域受益。本文通过回顾工业环境中机器人通信的现状，提出了一种观点，然后通过复杂性理论的视角对其进行批判性分析。此外，这项工作还指出了上述领域的研究空白，填补这些空白将推动该领域朝着真正自然的通信形式发展。最后，这项工作简要讨论了一个通用框架，该框架利用基于数据的技术的经验学习和人类知识的自然性。
