  In this paper, we report the results of our latest work on the automated
generation of planning operators from human demonstrations, and we present some
of our future research ideas. To automatically generate planning operators, our
system segments and recognizes different observed actions from human
demonstrations. We then proposed an automatic extraction method to detect the
relevant preconditions and effects from these demonstrations. Finally, our
system generates the associated planning operators and finds a sequence of
actions that satisfies a user-defined goal using a symbolic planner. The plan
is deployed on a simulated TIAGo robot. Our future research directions include
learning from and explaining execution failures and detecting cause-effect
relationships between demonstrated hand activities and their consequences on
the robot's environment. The former is crucial for trust-based and efficient
human-robot collaboration and the latter for learning in realistic and dynamic
environments.
