  We consider a sequential task and motion planning (tamp) setting in which a
robot is assigned continuous-space rearrangement-style tasks one-at-a-time in
an environment that persists between each. Lacking advance knowledge of future
tasks, existing (myopic) planning strategies unwittingly introduce side effects
that impede completion of subsequent tasks: e.g., by blocking future access or
manipulation. We present anticipatory task and motion planning, in which
estimates of expected future cost from a learned model inform selection of
plans generated by a model-based tamp planner so as to avoid such side effects,
choosing configurations of the environment that both complete the task and
minimize overall cost. Simulated multi-task deployments in
navigation-among-movable-obstacles and cabinet-loading domains yield
improvements of 32.7% and 16.7% average per-task cost respectively. When given
time in advance to prepare the environment, our learning-augmented planning
approach yields improvements of 83.1% and 22.3%. Both showcase the value of our
approach. Finally, we also demonstrate anticipatory tamp on a real-world Fetch
mobile manipulator.
