  Long horizon sequential manipulation tasks are effectively addressed
hierarchically: at a high level of abstraction the planner searches over
abstract action sequences, and when a plan is found, lower level motion plans
are generated. Such a strategy hinges on the ability to reliably predict that a
feasible low level plan will be found which satisfies the abstract plan.
However, computing Abstract Plan Feasibility (APF) is difficult because the
outcome of a plan depends on real-world phenomena that are difficult to model,
such as noise in estimation and execution. In this work, we present an active
learning approach to efficiently acquire an APF predictor through
task-independent, curious exploration on a robot. The robot identifies plans
whose outcomes would be informative about APF, executes those plans, and learns
from their successes or failures. Critically, we leverage an infeasible
subsequence property to prune candidate plans in the active learning strategy,
allowing our system to learn from less data. We evaluate our strategy in
simulation and on a real Franka Emika Panda robot with integrated perception,
experimentation, planning, and execution. In a stacking domain where objects
have non-uniform mass distributions, we show that our system permits real robot
learning of an APF model in four hundred self-supervised interactions, and that
our learned model can be used effectively in multiple downstream tasks.
