Visual Affordance Prediction for Guiding Robot Exploration

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'. Given an input image of a scene, we infer a distribution over plausible future states that can be achieved via interactions with it. To allow predicting diverse plausible futures, we discretize the space of continuous images with a VQ-VAE and use a Transformer-based model to learn a conditional distribution in the latent embedding space. We 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 evaluate the quality and diversity of the generations, and demonstrate how the trained affordance model can be used for guiding exploration during visual goal-conditioned policy learning in robotic manipulation.