  Learning policies that effectively utilize language instructions in complex,
multi-task environments is an important problem in sequential decision-making.
While it is possible to condition on the entire language instruction directly,
such an approach could suffer from generalization issues. In our work, we
propose \emph{Learning Interpretable Skill Abstractions (LISA)}, a hierarchical
imitation learning framework that can learn diverse, interpretable primitive
behaviors or skills from language-conditioned demonstrations to better
generalize to unseen instructions. LISA uses vector quantization to learn
discrete skill codes that are highly correlated with language instructions and
the behavior of the learned policy. In navigation and robotic manipulation
environments, LISA outperforms a strong non-hierarchical Decision Transformer
baseline in the low data regime and is able to compose learned skills to solve
tasks containing unseen long-range instructions. Our method demonstrates a more
natural way to condition on language in sequential decision-making problems and
achieve interpretable and controllable behavior with the learned skills.
