Title: One-Shot Learning of Multi-Step Tasks from Observation via Activity Localization in Auxiliary Video,
Abstract: Due to burdensome data requirements, learning from demonstration often falls
short of its promise to allow users to quickly and naturally program robots.
Demonstrations are inherently ambiguous and incomplete, making correct
generalization to unseen situations difficult without a large number of
demonstrations in varying conditions. By contrast, humans are often able to
learn complex tasks from a single demonstration (typically observations without
action labels) by leveraging context learned over a lifetime. Inspired by this
capability, our goal is to enable robots to perform one-shot learning of
multi-step tasks from observation by leveraging auxiliary video data as
context. Our primary contribution is a novel system that achieves this goal by:
(1) using a single user-segmented demonstration to define the primitive actions
that comprise a task, (2) localizing additional examples of these actions in
unsegmented auxiliary videos via a metalearning-based approach, (3) using these
additional examples to learn a reward function for each action, and (4)
performing reinforcement learning on top of the inferred reward functions to
learn action policies that can be combined to accomplish the task. We
empirically demonstrate that a robot can learn multi-step tasks more
effectively when provided auxiliary video, and that performance greatly
improves when localizing individual actions, compared to learning from
unsegmented videos.