  While robots can learn models to solve many manipulation tasks from raw
visual input, they cannot usually use these models to solve new problems. On
the other hand, symbolic planning methods such as STRIPS have long been able to
solve new problems given only a domain definition and a symbolic goal, but
these approaches often struggle on the real world robotic tasks due to the
challenges of grounding these symbols from sensor data in a
partially-observable world. We propose Deep Planning Domain Learning (DPDL), an
approach that combines the strengths of both methods to learn a hierarchical
model. DPDL learns a high-level model which predicts values for a large set of
logical predicates consisting of the current symbolic world state, and
separately learns a low-level policy which translates symbolic operators into
executable actions on the robot. This allows us to perform complex, multi-step
tasks even when the robot has not been explicitly trained on them. We show our
method on manipulation tasks in a photorealistic kitchen scenario.
