  A desirable property of autonomous agents is the ability to both solve
long-horizon problems and generalize to unseen tasks. Recent advances in
data-driven skill learning have shown that extracting behavioral priors from
offline data can enable agents to solve challenging long-horizon tasks with
reinforcement learning. However, generalization to tasks unseen during
behavioral prior training remains an outstanding challenge. To this end, we
present Few-shot Imitation with Skill Transition Models (FIST), an algorithm
that extracts skills from offline data and utilizes them to generalize to
unseen tasks given a few downstream demonstrations. FIST learns an inverse
skill dynamics model, a distance function, and utilizes a semi-parametric
approach for imitation. We show that FIST is capable of generalizing to new
tasks and substantially outperforms prior baselines in navigation experiments
requiring traversing unseen parts of a large maze and 7-DoF robotic arm
experiments requiring manipulating previously unseen objects in a kitchen.
