Hierarchical Imitation via Bayesian Meta-Learning

Designing robots that can learn multiple tasks is a difficult problem because learning from scratch for each task is prohibitively slow, leveraging experience from previous tasks might be difficult, and there might not exist a suitable reward function for RL. Thus some desirable properties are the ability to learn from demonstrations through imitation learning, which requires no reward function and can lead to exponential decreases in sample complexity, and to leverage past experience through meta-learning, which improves few-shot learning. Such robots would also benefit from leveraging hierarchy to re-use simple learned skills for more complex tasks, but learning hierarchical policies has historically proven difficult. In this work, we move closer towards satisfying these properties by formulating a hierarchical imitation learning problem which we tackle through meta-learning to leverage experience from multiple tasks. We present results on a linear point mass environment as well as a challenging simulated kitchen environment with a 7-DoF robotic arm.