  Motivated by the intuitive understanding humans have about the space of
possible interactions, and the ease with which they can generalize this
understanding to previously unseen scenes, we develop an approach for learning
visual affordances for guiding robot exploration. Given an input image of a
scene, we infer a distribution over plausible future states that can be
achieved via interactions with it. We use a Transformer-based model to learn a
conditional distribution in the latent embedding space of a VQ-VAE and show
that these models can be trained using large-scale and diverse passive data,
and that the learned models exhibit compositional generalization to diverse
objects beyond the training distribution. We show how the trained affordance
model can be used for guiding exploration by acting as a goal-sampling
distribution, during visual goal-conditioned policy learning in robotic
manipulation.
