  A robot deployed in a home over long stretches of time faces a true lifelong
learning problem. As it seeks to provide assistance to its users, the robot
should leverage any accumulated experience to improve its own knowledge and
proficiency. We formalize this setting with a novel formulation of lifelong
learning for task and motion planning (TAMP), which endows our learner with the
compositionality of TAMP systems. Exploiting the modularity of TAMP, we develop
a mixture of generative models that produces candidate continuous parameters
for a planner. Whereas most existing lifelong learning approaches determine a
priori how data is shared across various models, our approach learns shared and
non-shared models and determines which to use online during planning based on
auxiliary tasks that serve as a proxy for each model's understanding of a
state. Our method exhibits substantial improvements (over time and compared to
baselines) in planning success on 2D and BEHAVIOR domains.
