The future of work does not require a choice between human and robot. Aside from explicit human-robot collaboration, robotics can play an increasingly important role in helping train workers as well as the tools they may use, especially in complex tasks that may be difficult to automate or effectively roboticize. This paper introduces a form of smart tool for use by human workers and shows how training the tool for task recognition, one of the key requirements, can be accomplished. Machine learning (ML) with purely human-based data can be extremely laborious and time-consuming. First, we show how data synthetically-generated by a robot can be leveraged in the ML training process. Later, we demonstrate how fine-tuning ML models for individual physical tasks and workers can significantly scale up the benefits of using ML to provide this feedback. Experimental results show the effectiveness and scalability of our approach, as we test data size versus accuracy. Smart hand tools of the type introduced here can provide insights and real-time analytics on efficient and safe tool usage and operation, thereby enhancing human participation and skill in a wide range of work environments. Using robotic platforms to help train smart tools will be essential, particularly given the diverse types of applications for which smart hand tools are envisioned for human use.

翻译：工作的未来不需要在人类与机器人之间做出选择。除了显性的人机协作外，机器人还可以在培训工人及其可能使用的工具方面发挥日益重要的作用，尤其是在那些难以自动化或有效实现机器人化的复杂任务中。本文介绍了一种供人类工人使用的智能工具形式，并展示了如何实现该工具的核心需求之一——任务识别训练。仅依赖纯人类数据进行机器学习（ML）极为耗时费力。首先，我们展示了如何利用机器人合成的数据辅助ML训练过程。随后，我们论证了针对具体物理任务和工人对ML模型进行微调，能够显著扩大利用ML提供反馈的效益。实验结果通过测试数据规模与准确度的关系，证明了我们方法的有效性和可扩展性。本文所提出的这类智能手持工具，能够为高效安全的工具使用与操作提供洞察及实时分析，从而在广泛的工作环境中增强人类参与度和技能水平。考虑到智能手持工具在各类人类应用场景中的多样化设想，利用机器人平台辅助训练智能工具将至关重要。