  We describe a mobile manipulation hardware and software system capable of
autonomously performing complex human-level tasks in real homes, after being
taught the task with a single demonstration from a person in virtual reality.
This is enabled by a highly capable mobile manipulation robot, whole-body task
space hybrid position/force control, teaching of parameterized primitives
linked to a robust learned dense visual embeddings representation of the scene,
and a task graph of the taught behaviors. We demonstrate the robustness of the
approach by presenting results for performing a variety of tasks, under
different environmental conditions, in multiple real homes. Our approach
achieves 85% overall success rate on three tasks that consist of an average of
45 behaviors each.
