Abstract In human-robot collaborative assembly, robots are often required to dynamically change their pre-planned tasks to collaborate with human operators in a shared workspace. However, the robots used today are controlled by pre-generated rigid codes that cannot support effective human-robot collaboration. In response to this need, multi-modal yet symbiotic communication and control methods have been a focus in recent years. These methods include voice processing, gesture recognition, haptic interaction, and brainwave perception. Deep learning is used for classification, recognition and context awareness identification. Within this context, this keynote provides an overview of symbiotic human-robot collaborative assembly and highlights future research directions.

中文翻译：

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共生人机协同组装

摘要 在人机协同装配中，机器人经常需要动态改变其预先计划的任务，以便在共享工作空间中与人类操作员进行协作。然而，今天使用的机器人是由预先生成的刚性代码控制的，无法支持有效的人机协作。为了满足这一需求，多模态但共生的通信和控制方法已成为近年来的焦点。这些方法包括语音处理、手势识别、触觉交互和脑电波感知。深度学习用于分类、识别和上下文感知识别。在此背景下，本主题演讲概述了共生的人机协作组装，并强调了未来的研究方向。