We’ve developed an energy-based model that can quickly learn to identify and generate instances of concepts, such as near, above, between, closest, and furthest, expressed as sets of 2d points. Our model learns these concepts after only five demonstrations. We also show cross-domain transfer: we use concepts learned in a 2d particle environment to solve tasks on a 3-dimensional physics-based robot. Many hallmarks of human intelligence, such as generalizing from limited experience, abstract reasoning and planning, analogical reasoning, creative problem solving, and capacity for language require the ability to consolidate experience into concepts, which act as basic building blocks of understanding and reasoning. Our technique enables agents to learn and extract concepts from tasks, then use these concepts to solve other tasks in various domains. For example, our model can use concepts learned in a two-dimensional particle environment to let it carry out the same task on a three-dimensional physics-based robotic environment – without retraining in the new environment. A simulated robot trained via an energy-based model navigates its arm to be between two points, using a concept learned in a different 2D domain. This work uses energy functions to let our agents learn to classify and generate simple concepts, which they can use to solve tasks like navigating between two points in dissimilar environments. Examples of concepts include visual (‘red’ or ‘square’), spatial (‘inside’, ‘on top of’), temporal (‘slow’, ‘after’), social (‘aggressive’, ‘helpful’) among others. These concepts, once learned, act as basic building blocks of agent’s understanding and reasoning, as shown in other research from DeepMind and Vicarious.