  Classical policy search algorithms for robotics typically require performing
extensive explorations, which are time-consuming and expensive to implement
with real physical platforms. To facilitate the efficient learning of robot
manipulation skills, in this work, we propose a new approach comprised of three
modules: (1) learning of general prior knowledge with random explorations in
simulation, including state representations, dynamic models, and the
constrained action space of the task; (2) extraction of a state alignment-based
reward function from a single demonstration video; (3) real-time optimization
of the imitation policy under systematic safety constraints with sampling-based
model predictive control. This solution results in an efficient one-shot
imitation-from-video strategy that simplifies the learning and execution of
robot skills in real applications. Specifically, we learn priors in a scene of
a task family and then deploy the policy in a novel scene immediately following
a single demonstration, preventing time-consuming and risky explorations in the
environment. As we do not make a strong assumption of dynamic consistency
between the scenes, learning priors can be conducted in simulation to avoid
collecting data in real-world circumstances. We evaluate the effectiveness of
our approach in the context of contact-rich fabric manipulation, which is a
common scenario in industrial and domestic tasks. Detailed numerical
simulations and real-world hardware experiments reveal that our method can
achieve rapid skill acquisition for challenging manipulation tasks.
