  It is challenging learning from demonstrated observation-only trajectories in
a non-time-aligned environment because most imitation learning methods aim to
imitate experts by following the demonstration step-by-step. However, aligned
demonstrations are seldom obtainable in real-world scenarios. In this work, we
propose a new imitation learning approach called Hierarchical Imitation
Learning from Observation(HILONet), which adopts a hierarchical structure to
choose feasible sub-goals from demonstrated observations dynamically. Our
method can solve all kinds of tasks by achieving these sub-goals, whether it
has a single goal position or not. We also present three different ways to
increase sample efficiency in the hierarchical structure. We conduct extensive
experiments using several environments. The results show the improvement in
both performance and learning efficiency.
