  Integrated Task and Motion Planning (TMP) provides a promising class of
approaches for solving robot planning problems with intricate symbolic and
geometric constraints. However, the practical usefulness of TMP planners is
limited by their need for symbolic abstractions of robot actions, which are
difficult to construct even for experts. We propose an approach to
automatically construct and continuously improve a symbolic abstraction of a
robot action via observations of the robot performing the action. This
approach, called automatic abstraction repair, allows symbolic abstractions to
be initially incorrect or incomplete and converge toward a correct model over
time. Abstraction repair uses constrained polynomial zonotopes (CPZs), an
efficient non-convex set representation, to model predicates over joint
symbolic and geometric state, and performs an optimizing search over symbolic
edit operations to predicate formulae to improve the correspondence of a
symbolic abstraction to the behavior of a physical robot controller. In this
work, we describe the aforementioned predicate model, introduce the
symbolic-geometric abstraction repair problem, and present an anytime algorithm
for automatic abstraction repair. We then demonstrate that abstraction repair
can improve realistic action abstractions for common mobile manipulation
actions from a handful of observations.
