Learning Perceptual Prediction: Learning from Humans and Reasoning about Objects

Reasoning about the results of their actions is a critical skill for embodied agents. In this thesis, we study how robots can learn to predict the future from visual observations. We study two main questions. First, how can agents acquire sufficient data to train large prediction models? Second, what structure should we embed into these models? In this work, we address both questions. In order to acquire sufficient data, we demonstrate methods for learning prediction models and agent policies from the combination of human and robot data. By utilizing human data we are able to significantly increase the quantity of data available, improving prediction performance and task completion. We also investigate the question of what structure should be included in visual prediction models. We demonstrate an object-centric video prediction model that can learn to segment objects without any labels and show that the object-centric architecture allows for improved performance.