  Sampling-based planners are effective in many real-world applications such as
robotics manipulation, navigation, and even protein modeling. However, it is
often challenging to generate a collision-free path in environments where key
areas are hard to sample. In the absence of any prior information,
sampling-based planners are forced to explore uniformly or heuristically, which
can lead to degraded performance. One way to improve performance is to use
prior knowledge of environments to adapt the sampling strategy to the problem
at hand. In this work, we decompose the workspace into local primitives,
memorizing local experiences by these primitives in the form of local samplers,
and store them in a database. We synthesize an efficient global sampler by
retrieving local experiences relevant to the given situation. Our method
transfers knowledge effectively between diverse environments that share local
primitives and speeds up the performance dramatically. Our results show, in
terms of solution time, an improvement of multiple orders of magnitude in two
traditionally challenging high-dimensional problems compared to
state-of-the-art approaches.
