Energy-based models are undergoing a resurgence of interest, but their applications have largely focused on generative modeling and density estimation. In this talk I will discuss application of energy-based models to object or concept oriented learning and reasoning. These models offer an elegant approach to concept composition, continual and unsupervised learning, and usage of concepts in multiple contexts. I will show examples of these advantages, and conclude with a set of future research directions.