Planning Hierarchies and their Connections to Language

Robots working with humans in real environments need to plan in a large state--action space given a natural language command. Such a problem poses multiple challenges with respect to the size of the state--action space to plan over, the different modalities that natural language can provide to specify the goal condition, and the difficulty of learning a model of such an environment to plan over. In this thesis we would look at using hierarchical methods to learn and plan in these large state--action spaces. Further, we would look the using natural language to guide the construction and learning of hierarchies and reward functions.