  Conventional works that learn grasping affordance from demonstrations need to
explicitly predict grasping configurations, such as gripper approaching angles
or grasping preshapes. Classic motion planners could then sample trajectories
by using such predicted configurations. In this work, our goal is instead to
fill the gap between affordance discovery and affordance-based policy learning
by integrating the two objectives in an end-to-end imitation learning framework
based on deep neural networks. From a psychological perspective, there is a
close association between attention and affordance. Therefore, with an
end-to-end neural network, we propose to learn affordance cues as visual
attention that serves as a useful indicating signal of how a demonstrator
accomplishes tasks, instead of explicitly modeling affordances. To achieve
this, we propose a contrastive learning framework that consists of a Siamese
encoder and a trajectory decoder. We further introduce a coupled triplet loss
to encourage the discovered affordance cues to be more affordance-relevant. Our
experimental results demonstrate that our model with the coupled triplet loss
achieves the highest grasping success rate in a simulated robot environment.
Our project website can be accessed at
https://sites.google.com/asu.edu/affordance-aware-imitation/project.
