  When cast into the Deep Reinforcement Learning framework, many robotics tasks
require solving a long horizon and sparse reward problem, where learning
algorithms struggle. In such context, Imitation Learning (IL) can be a powerful
approach to bootstrap the learning process. However, most IL methods require
several expert demonstrations which can be prohibitively difficult to acquire.
Only a handful of IL algorithms have shown efficiency in the context of an
extreme low expert data regime where a single expert demonstration is
available. In this paper, we present a novel algorithm designed to imitate
complex robotic tasks from the states of an expert trajectory. Based on a
sequential inductive bias, our method divides the complex task into smaller
skills. The skills are learned into a goal-conditioned policy that is able to
solve each skill individually and chain skills to solve the entire task. We
show that our method imitates a non-holonomic navigation task and scales to a
complex simulated robotic manipulation task with very high sample efficiency.
