  Planners using accurate models can be effective for accomplishing
manipulation tasks in the real world, but are typically highly specialized and
require significant fine-tuning to be reliable. Meanwhile, learning is useful
for adaptation, but can require a substantial amount of data collection. In
this paper, we propose a method that improves the efficiency of sub-optimal
planners with approximate but simple and fast models by switching to a
model-free policy when unexpected transitions are observed. Unlike previous
work, our method specifically addresses when the planner fails due to
transition model error by patching with a local policy only where needed.
First, we use a sub-optimal model-based planner to perform a task until model
failure is detected. Next, we learn a local model-free policy from expert
demonstrations to complete the task in regions where the model failed. To show
the efficacy of our method, we perform experiments with a shape insertion
puzzle and compare our results to both pure planning and imitation learning
approaches. We then apply our method to a door opening task. Our experiments
demonstrate that our patch-enhanced planner performs more reliably than pure
planning and with lower overall sample complexity than pure imitation learning.
