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：人机协作是实现柔性制造、满足客户多样化需求的关键要素之一。然而，由于人类行为的多样性与数据稀缺性，构建能够以安全高效方式主动协助人类的智能机器人仍面临诸多挑战。首先，人类行为的差异性和数据匮乏使得高效协作难以实现；其次，人类行为的不确定性使交互安全难以保障。本文提出了一种面向主动性人机协作的集成框架。该框架通过融合先验任务信息与人机协同训练，构建了鲁棒意图预测模块，用于引导机器人实现高效协作。同时，框架采用鲁棒安全控制策略，确保不确定性条件下的交互安全。我们将该框架应用于Kinova Gen3机器人执行的协同装配任务。实验结果表明，该方案对环境变化及不同人类偏好与行为均具有鲁棒性，任务效率提升约15-20%。此外，实验证实该方案能够在主动协作过程中保障交互安全性。