Human-robot collaboration (HRC) is one key component to achieving flexible manufacturing to meet the different needs of customers. However, it is difficult to build intelligent robots that can proactively assist humans in a safe and efficient way due to several challenges. First, it is challenging to achieve efficient collaboration due to diverse human behaviors and data scarcity. Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors. This paper presents an integrated framework for proactive HRC. A robust intention prediction module, which leverages prior task information and human-in-the-loop training, is learned to guide the robot for efficient collaboration. The proposed framework also uses robust safe control to ensure interactive safety under uncertainty. The developed framework is applied to a co-assembly task using a Kinova Gen3 robot. The experiment demonstrates that our solution is robust to environmental changes as well as different human preferences and behaviors. In addition, it improves task efficiency by approximately 15-20%. Moreover, the experiment demonstrates that our solution can guarantee interactive safety during proactive collaboration.

翻译：人机协作（HRC）是实现柔性制造以满足客户多样化需求的关键组成部分。然而，由于若干挑战，难以构建能够以安全高效方式主动辅助人类的智能机器人。首先，人类行为的多样性及数据稀缺性使得高效协作难以实现；其次，人类行为的不确定性导致交互安全性难以保障。本文提出了一种面向主动式人机协作的集成框架。该框架通过利用先验任务信息与人在回路训练方法，学习得到一个鲁棒意图预测模块，用于引导机器人实现高效协作。同时，所提框架采用鲁棒安全控制机制，确保在不确定性条件下的交互安全性。该框架被应用于基于Kinova Gen3机器人的协同装配任务中。实验表明，我们的解决方案对环境变化以及不同人类偏好和行为具有鲁棒性。此外，该方法可将任务效率提升约15-20%。实验还证明，该方案能够在主动协作过程中保障交互安全性。