  Earlier work has shown that reusing experience from prior motion planning
problems can improve the efficiency of similar, future motion planning queries.
However, for robots with many degrees-of-freedom, these methods exhibit poor
generalization across different environments and often require large datasets
that are impractical to gather. We present SPARK and FLAME , two
experience-based frameworks for sampling-based planning applicable to complex
manipulators in 3 D environments. Both combine samplers associated with
features from a workspace decomposition into a global biased sampling
distribution. SPARK decomposes the environment based on exact geometry while
FLAME is more general, and uses an octree-based decomposition obtained from
sensor data. We demonstrate the effectiveness of SPARK and FLAME on a Fetch
robot tasked with challenging pick-and-place manipulation problems. Our
approaches can be trained incrementally and significantly improve performance
with only a handful of examples, generalizing better over diverse tasks and
environments as compared to prior approaches.
