  Robot learning from demonstration (LfD) is a research paradigm that can play
an important role in addressing the issue of scaling up robot learning. Since
this type of approach enables non-robotics experts can teach robots new
knowledge without any professional background of mechanical engineering or
computer programming skills, robots can appear in the real world even if it
does not have any prior knowledge for any tasks like a new born baby. There is
a growing body of literature that employ LfD approach for training robots. In
this paper, I present a survey of recent research in this area while focusing
on studies for human-robot collaborative tasks. Since there are different
aspects between stand-alone tasks and collaborative tasks, researchers should
consider these differences to design collaborative robots for more effective
and natural human-robot collaboration (HRC). In this regard, many researchers
have shown an increased interest in to make better communication framework
between robots and humans because communication is a key issue to apply LfD
paradigm for human-robot collaboration. I thus review some recent works that
focus on designing better communication channels/methods at the first, then
deal with another interesting research method, Interactive/Active learning,
after that I finally present other recent approaches tackle a more challenging
problem, learning of complex tasks, in the last of the paper.
