  We introduce a Learning from Demonstration (LfD) approach for contact-rich
manipulation tasks with articulated mechanisms. The extracted policy from a
single human demonstration generalizes to different mechanisms of the same type
and is robust against environmental variations. The key to achieving such
generalization and robustness from a single human demonstration is to
autonomously augment the initial demonstration to gather additional information
through purposefully interacting with the environment. Our real-world
experiments on complex mechanisms with multi-DOF demonstrate that our approach
can reliably accomplish the task in a changing environment. Videos are
available at the: https://sites.google.com/view/rbosalfdec/home
