Divide & Conquer Imitation Learning
This addresses the challenge of imitation learning in robotics for scenarios with only a single expert demonstration, which is incremental but improves sample efficiency.
The paper tackles the problem of learning complex robotic tasks with sparse rewards and limited expert demonstrations by dividing the task into smaller skills and learning a goal-conditioned policy. It demonstrates high sample efficiency in imitating non-holonomic navigation and simulated robotic manipulation tasks.
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.