Video2Skill: Adapting Events in Demonstration Videos to Skills in an
  Environment using Cyclic MDP Homomorphisms
  Humans excel at learning long-horizon tasks from demonstrations augmented
with textual commentary, as evidenced by the burgeoning popularity of tutorial
videos online. Intuitively, this capability can be separated into 2 distinct
subtasks - first, dividing a long-horizon demonstration sequence into
semantically meaningful events; second, adapting such events into meaningful
behaviors in one's own environment. Here, we present Video2Skill (V2S), which
attempts to extend this capability to artificial agents by allowing a robot arm
to learn from human cooking videos. We first use sequence-to-sequence
Auto-Encoder style architectures to learn a temporal latent space for events in
long-horizon demonstrations. We then transfer these representations to the
robotic target domain, using a small amount of offline and unrelated
interaction data (sequences of state-action pairs of the robot arm controlled
by an expert) to adapt these events into actionable representations, i.e.,
skills. Through experiments, we demonstrate that our approach results in
self-supervised analogy learning, where the agent learns to draw analogies
between motions in human demonstration data and behaviors in the robotic
environment. We also demonstrate the efficacy of our approach on model learning
- demonstrating how Video2Skill utilizes prior knowledge from human
demonstration to outperform traditional model learning of long-horizon
dynamics. Finally, we demonstrate the utility of our approach for non-tabula
rasa decision-making, i.e, utilizing video demonstration for zero-shot skill
generation.
