  One-shot imitation is to learn a new task from a single demonstration, yet it
is a challenging problem to adopt it for complex tasks with the high domain
diversity inherent in a non-stationary environment. To tackle the problem, we
explore the compositionality of complex tasks, and present a novel skill-based
imitation learning framework enabling one-shot imitation and zero-shot
adaptation; from a single demonstration for a complex unseen task, a semantic
skill sequence is inferred and then each skill in the sequence is converted
into an action sequence optimized for environmental hidden dynamics that can
vary over time. Specifically, we leverage a vision-language model to learn a
semantic skill set from offline video datasets, where each skill is represented
on the vision-language embedding space, and adapt meta-learning with dynamics
inference to enable zero-shot skill adaptation. We evaluate our framework with
various one-shot imitation scenarios for extended multi-stage Meta-world tasks,
showing its superiority in learning complex tasks, generalizing to dynamics
changes, and extending to different demonstration conditions and modalities,
compared to other baselines.
