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：未来工作无需在人类与机器人之间做出取舍。除了显性的人机协作外，机器人可在培训工人及其使用工具方面发挥日益重要的作用，尤其适用于难以实现自动化或有效机器化的复杂任务。本文介绍一种供人类工人生成的智能工具形式，并展示如何完成工具的任务识别训练——这是关键需求之一。基于纯人类数据的机器学习（ML）可能极其耗时费力。首先，我们展示如何利用机器人生成的合成数据辅助机器学习训练过程。随后论证：针对具体物理任务和工人对机器学习模型进行微调，可显著提升利用ML提供反馈的效益。实验结果表明该方法在有效性与可扩展性方面的优势，我们测试了数据规模与准确率的对应关系。本文提出的智能手持工具可为高效安全工具使用与操作提供洞察与实时分析，从而在广泛工作场景中增强人类参与度与技能。考虑到人类用智能手持工具预期应用的多样性，利用机器人平台辅助训练智能工具将至关重要。