Dobb·E is an open-source framework for teaching robots household tasks via imitation learning in 20 minutes. The framework uses a simple tool called the Stick, collects a dataset called Homes of New York (HoNY) with it, and then trains a representation learning model, Home Pretrained Representations (HPR) with it. Then, Dobb·E uses five minutes of collected data in a new home for a novel task and within fifteen minutes, gives a policy that can solve the task with 81% average success rate.