  Learning a robot motor skill from scratch is impractically slow; so much so
that in practice, learning must be bootstrapped using a good skill policy
obtained from human demonstration. However, relying on human demonstration
necessarily degrades the autonomy of robots that must learn a wide variety of
skills over their operational lifetimes. We propose using kinematic motion
planning as a completely autonomous, sample efficient way to bootstrap motor
skill learning for object manipulation. We demonstrate the use of motion
planners to bootstrap motor skills in two complex object manipulation scenarios
with different policy representations: opening a drawer with a dynamic movement
primitive representation, and closing a microwave door with a deep neural
network policy. We also show how our method can bootstrap a motor skill for the
challenging dynamic task of learning to hit a ball off a tee, where a kinematic
plan based on treating the scene as static is insufficient to solve the task,
but sufficient to bootstrap a more dynamic policy. In all three cases, our
method is competitive with human-demonstrated initialization, and significantly
outperforms starting with a random policy. This approach enables robots to to
efficiently and autonomously learn motor policies for dynamic tasks without
human demonstration.
