  Imitation learning (IL) algorithms have shown promising results for robots to
learn skills from expert demonstrations. However, they need multi-task
demonstrations to be provided at once for acquiring diverse skills, which is
difficult in real world. In this work we study how to realize continual
imitation learning ability that empowers robots to continually learn new tasks
one by one, thus reducing the burden of multi-task IL and accelerating the
process of new task learning at the same time. We propose a novel trajectory
generation model that employs both a generative adversarial network and a
dynamics-aware prediction model to generate pseudo trajectories from all
learned tasks in the new task learning process. Our experiments on both
simulation and real-world manipulation tasks demonstrate the effectiveness of
our method.
