Language-guided Skill Learning with Temporal Variational Inference
Haotian Fu Pratyusha Sharma Elias Stengel-Eskin George Konidaris Nicolas Le Roux Marc-Alexandre Côté Xingdi Yuan
We present an algorithm for skill discovery from expert demonstrations. The
algorithm first utilizes Large Language Models (LLMs) to propose an initial
segmentation of the trajectories. Following that, a hierarchical variational
inference framework incorporates the LLM-generated segmentation information to
discover reusable skills by merging trajectory segments. To further control the
trade-off between compression and reusability, we introduce a novel auxiliary
objective based on the Minimum Description Length principle that helps guide
this skill discovery process. Our results demonstrate that agents equipped with
our method are able to discover skills that help accelerate learning and
outperform baseline skill learning approaches on new long-horizon tasks in
BabyAI, a grid world navigation environment, as well as ALFRED, a household
simulation environment.