Abstract
Watching expert demonstrations is an important way for
humans and robots to reason about affordances of unseen
objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding
of demonstration videos. We design the Demo2Vec model
which learns to extract embedded vectors of demonstration
videos and predicts the interaction region and the action
label on a target image of the same object. We introduce
the Online Product Review dataset for Affordance (OPRA)
by collecting and labeling diverse YouTube product review
videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset