  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.
