A new AI learning method called Adaptive Agents via Latent Topologies (AALT) is introduced, which improves active imitation learning by selecting demonstrations that maximize expected gains in start-goal connectivity. This approach is shown to be effective in a simulated robot domain, with AALT achieving 100% success in 72 tasks using only 3 demonstrations.